Workshops
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The striding advances of computer vision techniques are revolutionizing many long-standing automatic medical diagnosis tasks. Emerging trends—such as Large Language Models (LLMs), Foundation Models (FMs), advanced learning paradigms (e.g., un-/semi-/self-supervised learning), and considerations of fairness and generalization—remain underexplored for secure and reliable automated medical diagnosis. Distinctly, this workshop emphasizes integrating insights from clinicians and radiologists alongside technical discussions to better advance the field.
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Generative AI allows for the rapid and automatic generation of highly realistic audio, images, and videos (so-called deepfakes). The field of media forensics and digital provenance focus on detection and authentication of this content, thus helping in mitigating the potential risks. This workshop aims at bringing a heterogeneous group of specialists from academia, industry, and civil society together to discuss emerging threats, technologies, and mitigation strategies. The workshop will focus on the application of tools from computer vision, pattern recognition, and machine learning, as well as the development of novel approaches for verifying the integrity and tracing the origins of digital media, the creation of novel datasets for evaluation, large-scale evaluations of existing forensic techniques, and ethical/policy considerations around generative AI and forensic techniques.
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The Eighth International Workshop on Computer Vision for Physiological Measurement (CVPM) is the top venue for research on computer vision methods for measuring and modeling physiological processes. The goal of the workshop is to bridge the disciplines of computer vision and biomedical science and help effectively translate advances in AI into practice.
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The world is three-dimensional. This fact was first seen by trilobites, the first organisms capable of sensing light. From that moment, nervous systems began to evolve, gradually transforming mere sight into insight, understanding, and action. All these combined gives rise to intelligence. Despite remarkable technological advancements in recent decades, modern embodied systems remain far from achieving full intelligence. They fall short in several key aspects: (i) contain information necessary for physical interaction, such as temporal dynamics of the scene; (ii) have a prior over semantic relevance, and should focus on task-relevant features like objects and their relationships; and (iii) be compact, avoiding the inclusion of irrelevant details, such as background elements. Attempts have been made, including integrating foundational models and utilizing large-scale data. Yet, the path to true intelligence remains long, with significant progress still required.
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The ABAW Workshop is a premier platform highlighting the latest advancements in multimodal analysis, generation, modeling, and understanding of human behavior in unconstrained environments. It emphasizes cutting-edge systems that integrate facial expressions, body movements, gestures, natural language, voice to enable impactful research and practical applications. The workshop fosters interdisciplinary collaboration across fields (e.g. computer vision, AI, HCI, psychology, robotics, ethics & healthcare) and is a vital forum for building equitable, generalizable & human-centered AI systems. Finally, the Workshop also includes 3 challenges (Valence-Arousal Estimation, Compound Expression Recognition and Fine-Grained Violence Detection).
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Recently, transformer-based foundation models have excelled across a wide range of recognition and generation benchmarks, yet real industrial impact requires robust tech transfer. Adapting them to heterogeneous industries demands domain-specific fine-tuning, reliable MLOps, and abundant, high-quality data. Conventional IID benchmarks are increasingly saturated, prompting evaluations that probe out-of-distribution and long-tail behavior. Both challenges hinge on curating and exploiting broader, deeper — “Foundation Data.” This workshop gathers academia and industry to examine methods for constructing high-quality datasets, refine model-adaptation pipelines, and design novel evaluation tasks grounded in Foundation Data, aiming to unlock new horizons in AI research and application.
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The 2nd Workshop on the Challenge Of Out-Of-Label Hazards in Autonomous Driving (2COOOL) focuses on enhancing safety and robustness in autonomous systems by tackling challenges posed by unknown or out-of-distribution objects and behaviors. The workshop brings together researchers and practitioners from academia and industry, including our diverse team of co-organizers, to explore state-of-the-art solutions and techniques across various domains for real-world driving environments. It features expert keynotes, paper presentations, and a Kaggle challenge on generating hazard and accident reports from dashboard cameras. 2COOOL aims to advance the frontier of autonomous driving by fostering innovation to handle unexpected scenarios and build more robust ADAS systems.
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The workshop aims to unite researchers and practitioners at the intersection of vision-based AI and large language models (LLMs) to advance digital health innovation. By showcasing deep‑learning applications on high‑resolution imaging modalities (e.g., MRI, CT, retinal photography), we’ll explore how early disease detection and automated image review can boost diagnostic accuracy and streamline clinical workflows. We’ll also delve into emerging “Vision + LLM” systems that fuse visual understanding with natural‑language capabilities for automated report generation, intelligent literature retrieval, and interactive decision support. Through presentations and discussions, participants will identify challenges, exchange best practices, and chart pathways toward more personalized, data‑driven care.
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The STREAM Workshop aims to bring together researchers and practitioners working at the intersection of systems design and trustworthy AI. As AI technologies are increasingly deployed in critical domains such as healthcare, finance, and mobility, STREAM focuses on system-level approaches to embedding trustworthiness across the full pipeline, from data collection and architecture design to training, deployment, and evaluation.
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The 8th BMTT Workshop focuses on action-aware multi-object tracking, aiming to unify temporal action localization and object tracking through natural language queries. While existing benchmarks often address these tasks separately, this workshop presents unified challenges to evaluate both capabilities. Participants are encouraged to develop models that can understand complex actions, follow detailed language instructions, and track multiple objects across time. The workshop aims to close the gap between vision and language, advancing multimodal video understanding and supporting research on scalable, real-world systems capable of fine-grained, action-driven reasoning in dynamic scenes.
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The Computer Vision for Developing Countries (CV4DC) workshop aims to create a supportive environment where students/researchers in the field of computer vision and related areas in AI can connect with each other, share their latest work, and expand their network for potential future collaborations and mentorships. This workshop empowers students and researchers from underrepresented, developing countries by providing opportunities to network, learn from the field experts, and share their work. We believe giving opportunities to students and researchers from lesser-known countries will foster diversity in computer research, leading to richer and more innovative contributions to the field.
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This workshop focuses on the closely related problems of camera calibration and pose estimation. These are essential for many advanced 3D computer vision methods, including NeRFs, 3D Gaussian splatting, and scene understanding. The quality of these estimates greatly affects performance, yet many researchers treat them as black boxes. This workshop offers an opportunity for those using calibration and pose algorithms to learn about the latest methods and open challenges. It also provides a forum for researchers working on traditional and learning-based solutions to share ideas, improve methods, and expand the possibilities of 3D vision through better calibration and pose estimation.
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The Computer Vision for Fashion, Art, and Design workshop series aims to to foster interdisciplinary discussions among researchers and practitioners in computer vision and machine learning, as well as artists, designers, sociotechnical researchers, policymakers, social scientists, and other cultural stakeholders. By creating a collaborative space, it aims to address complex challenges that arise at the intersection of generative AI, creativity, and ethics. This year the workshop includes, in addition to multiple invited talks by scientists working in the field, an Art Gallery, and a related Panel discussion.
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This workshop focuses on the foundational challenges of building AI systems that are unbiased, interpretable, and trustworthy. It aims to uncover the origins of algorithmic and data bias, advance the science of interpretability, and explore rigorous evaluation methods to ensure AI reliability. By bringing together researchers across biomedical imaging and signal processing, the workshop highlights novel methodologies and theoretical insights, emphasizing UIT as a scientific discipline rather than just an application concern. The event will showcase recent advances and foster discussions on future directions for inherently fair and transparent AI systems.
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The 4th DataCV Workshop focuses on advancing data-centric perspectives in computer vision, shifting attention from algorithm-centric research to the analysis and understanding of vision datasets. We aim to explore dataset-level properties, representations, and similarities, as well as challenges in bias, fairness, and generalization. Topics include evaluating vision-language models, improving dataset quality through simulation, and reducing reliance on labeled data. The workshop encourages research on how dataset insights can guide model development, performance prediction, and ethical considerations. By fostering discussion and innovation in dataset analysis, DataCV promotes more robust, generalizable, and responsible vision systems.
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This workshop aims to examine the state of the field of explainable AI (XAI) for computer vision, with the following goals: (1) discussion and dissemination of ideas at the cutting-edge of XAI research, and (2) a critical introspection on the challenges faced by the community and the way forward. The workshop includes papers, talks on recent advances, and a formal debate among invited speakers on the field’s core issues. We hope to encourage brainstorming in the community to bridge the gap from theory to practice and address challenges brought forth by the rise of large-scale foundation models, such as fundamentally rethinking what one wants from an explanation, obtaining it, performing appropriate evaluations, complying with regulatory requirements, and maintaining model performance.
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The 3rd Perception Test challenge comprehensively evaluates the perception capabilities of large multimodal models using the Perception Test benchmark. This year, novel tracks unify diverse tasks under common interfaces: joint object/point tracking, joint action/sound localisation, and unified multiple-choice videoQA (integrating non-semantic tasks via inpainted queries). A new VLM interpretability track is included to investigate model strengths and failures. Guest tracks cover image understanding (KiVA) and video generation (Physics-IQ). Our workshop provides a venue to evaluate all foundation vision models—discriminative, generative, image- or video-based. Prizes up to 50k EUR are available.
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This workshop mainly looks at multi-modal scene understanding and perception in a human-like manner. Specifically, we will focus on binocular/stereo egocentric and 360° panoramic perspectives, which measure both first-person views and third-person panoptic views, mimicking a human in the scene, by combining with multi‑modal cues such as spatial audio, textual descriptions, and geo‑metadata. This workshop will cover but not be limited to the following topics: Embodied 360° scene understanding & egocentric visual reasoning; Multi-modal scene understanding; Stereo Vision; Open‑world learning & domain adaptation.
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DriveX explores the integration of foundation models and V2X-based cooperative systems to improve perception, planning, and decision-making in autonomous vehicles. While traditional single-vehicle systems have advanced tasks like 3D object detection, emerging challenges like holistic scene understanding and 3D occupancy prediction require more comprehensive solutions. Collaborative driving systems, utilizing V2X communication and roadside infrastructure, extend sensory range, provide hazard warnings, and improve decision-making through shared data. Simultaneously, Vision-Language Models (VLMs) offer generalization abilities, enabling zero-shot learning, open-vocabulary recognition, and scene explanation for novel scenarios. DriveX aims to bring together experts to explore these technologies, address challenges, and advance road safety.
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While Human-Centric Foundation Models (HFM) excel at perceiving and generating human data, they remain passive, struggling with real-time interaction and adaptation. This limits real-world deployment. The emerging field of Interactive HFM (I-HFM) addresses this by enabling bidirectional engagement. I-HFMs operate across three critical dimensions: (a) interacting with users for intuitive content creation/refinement, (b) interacting with environments to learn and adapt like humans, and (c) interacting with other agents for collaborative task-solving. This interactivity transforms AI from passive models into proactive, human-like agents, bridging the gap towards responsive, socially intelligent AGI that integrates seamlessly into human societies.
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Disentangled Representation Learning shows promise for enhancing AI's fundamental understanding of the world, potentially addressing hallucination issues in language models and improving controllability in generative systems. Despite significant academic interest, DRL research remains confined to synthetic scenarios due to a lack of realistic benchmarks and unified evaluation metrics.
DRL4Real Workshop aims to bridge this gap by introducing novel, realistic datasets and comprehensive benchmarks for evaluating DRL methods in practical applications. We will focus on key areas including controllable generation and autonomous driving, exploring how DRL can advance model robustness, interpretability, and generalization capabilities.
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Single-photon cameras are an emerging class of camera technology with the potential to revolutionize the way today’s computer vision systems capture and process scene information, thanks to their extreme sensitivity, high speed capabilities, and increasing commercial availability. These cameras can be used for a wide range of applications: self-driving cars and autonomous robots, high-sensitivity cameras for night photography and fluorescence-guided surgeries, and high dynamic range cameras for industrial machine vision and biomedical imaging applications. This workshop will showcase the myriad ways in which single-photon cameras are used today in computer vision and inspire new unexplored applications.
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Memory is a core aspect of human intelligence, and artificial memory systems have recently seen a resurgence through foundational breakthroughs. At the same time, advances in computer vision, especially through generative AI, have enabled models to synthesize realistic imagery and understand complex scenes with remarkable generalization. Despite their shared relevance to cognition, memory and vision have evolved largely as separate fields. MemVis is the dedicated platform organized around the growing need to unify memory and vision, in the development of intelligent AI systems that can process, store, and recall visual information in a more human-like manner.
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The Instance-Level Recognition and Generation (ILR+G) Workshop focuses on computer vision tasks that operate at instance-level granularity, covering both recognition (ILR) and generation (ILG). Unlike category-level, ILR identifies and compares specific objects, scenes, or events, enabling open-world applications with a vast number of distinct classes. ILG, or personalized generation, aims to create content while preserving the identity of particular instances. This 7th edition explores potential synergies between ILR and ILG. The workshop features keynote talks by renowned speakers, invited papers, and a call for papers, aiming to bring together researchers working on instance-level tasks and inspire new research and collaborations.
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This workshop focuses on recent advances in video generative models and their applications in 3D and 4D generation and reconstruction. Topics include camera- and motion-controlled video synthesis, large-scale 3D/4D reconstruction, neural rendering, and generative model-guided pipelines. A central focus is geometry-free novel view synthesis with video diffusion models, enabling spatial control without explicit 3D geometry. The program also covers the distillation of temporal models into spatial representations. By highlighting these developments, the workshop aims to chart the path toward more controllable, photorealistic, and efficient generative pipelines that unify video generation with 3D and 4D reconstruction.
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The VISION workshop will provide a platform for the exchange of scholarly innovations and emerging practical challenges in Vision-based Industrial Inspection. Through a series of keynote talks, technical presentations, and challenge competitions, this workshop aims to (i) bring together researchers from the interdisciplinary research communities related to computer vision-based inspection; and (ii) connect researchers and industry practitioners to synergize recent research progress and current needs in industrial practice.
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The DeepID challenge aims to advance state of the art in detection of digitally manipulated ID documents. Recent increase in fraudulent attempts to bypass know your customer (KYC) services with generated or manipulated images of ID documents calls for automated robust detection methods. In this challenge, we provided participants with a training dataset of fantasy ID cards containing both bona fide and manipulated samples (faces swapped and text inpainted). For evaluation, we created a separate test set of fantasy ID cards and also a private 20K set of real world ID documents with genuine bona fide and digitally manipulated versions. We evaluated the docker submissions from more than 25 participated teams using an air gapped machine on the two datasets. The workshop will feature two keynote talks from renown researchers in media forensics and the top winning teams of the challenge.
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In recent years, there has been a growing trend toward training data-centric, large-scale foundation models that reduce reliance on structural priors. However, is simply scaling up Transformers truly the ultimate solution for computer vision? In this workshop, we aim to reintroduce structural priors and explore how they can further push the boundaries of foundation models. Our workshop provides an interdisciplinary space for sharing ideas across domains. For example, scene-aware 2D perception can enhance 3D modeling and robotic manipulation, while geometric reasoning can enhance the visual grounding of 2D perception and multimodal models. Through these interactions, we aim to better define the role of priors in vision foundation models.
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The SLoMO workshop brings together researchers focused on the understanding of long-form, edited videos—such as movies and TV episodes. We spotlight two central research directions: (i) Audio Description (AD) Generation: This track explores the generation of concise and coherent descriptions that complement the original audio for blind and visually impaired (BVI) audiences. We have invited four leading experts in movie understanding and AD generation to share their insights and recent advancements in the field. (ii) Movie Question Answering: This track evaluates models’ capabilities in narrative comprehension, emphasizing story-level understanding. As part of this effort, we host the Short-Films 20K (SF20K) Competition, which aims to drive progress in story-level video understanding using the newly introduced SF20K dataset.
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Seamless integration of audio and visual elements is crucial for creating immersive and engaging content. Audio-visual generation, involving the synthesis of one modality from the other or both jointly, has become a key research area. This capability holds significant potential for applications like virtual reality, gaming, film production, and interactive media, using advanced generative models to enhance multimedia quality and realism. This workshop highlights the growing importance of audio-visual generation in modern content creation, bringing together researchers and practitioners from academia and industry to explore the latest advances, challenges, and emerging opportunities in this dynamic field.
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This workshop brings together researchers in autonomous driving, computer vision, and graphics to advance the development of real-world data-driven driving simulators, as well as the autonomous driving algorithms in these photorealistic simulation environments. By tackling novel view synthesis and closed-loop autonomy in photorealistic simulations, we aim to push scalable, high-fidelity simulation forward. To promote community engagement and benchmarking, we also host two challenges: extrapolated novel view synthesis for urban scenes and closed-loop evaluation in photorealistic simulators.
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The rapid evolution of generative AI has reshaped content creation across images, video, and 3D/4D visuals. This workshop focuses on cutting-edge methodologies, practical applications, and open challenges in image/video/3D/4D generation and related editing tasks with an emphasis on flexible and friendly human interactions and multi-modal control signals. This workshop will serve as a platform for researchers and practitioners to discuss key topics related to visual content creation and editing with versatile interactions.
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The AI4VA workshop at ICCV explores the intersection of artificial intelligence and the visual arts, including art, design, exhibitions, photography, and film. It brings together artists, art historians, ethicists, and researchers to foster cross-disciplinary innovation. Topics include generative art, AI for art history, 3D reconstruction from artworks, human pose estimation in art, VQA and captioning for artworks, multimodal interaction, AR/VR for art, and multimedia content analysis. A key aim is fostering participation across diverse creators and researchers. A special focus is AI for Cultural and Artistic Heritage, highlighting advances in analysing, restoring, and interpreting artefacts using multimodal AI across visual, textual, and historical data.
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Multi-modal Localization and Mapping is an essential component of computer vision, with diverse applications in fields such as autonomous robotics, augmented reality, and beyond. This workshop aims to unite researchers, practitioners, and enthusiasts to explore the latest advancements, challenges, and innovations in multi-modal localization and mapping. By leveraging information from various sensors (e.g. camera, IMU, LiDAR, radar, and language), multi-modal approaches can significantly enhance localization and mapping accuracy in complex environments.
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Multimodal systems are transforming AI by enabling models to understand and act across language, vision, and other modalities, driving advances in robotics, autonomous driving, and scientific discovery. However, these capabilities raise serious safety and trustworthiness concerns, as traditional safeguards often fall short in multimodal contexts. The Workshop on Safe and Trustworthy Multimodal AI Systems (SaFeMM-AI) at ICCV 2025 brings together the computer vision community to address challenges including hallucinations, privacy leakage, and jailbreak vulnerabilities, and to promote the development of safer, more robust, and reliable multimodal models that can handle unsafe or adversarial inputs and consistently produce trustworthy outputs.
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Embedded vision is an active field of research, bringing together efficient learning models with fast computer vision and pattern recognition algorithms, to tackle many areas of robotics and intelligent systems that are enjoying an impressive growth today. Such strong impact comes with many challenges that stem from the difficulty of understanding complex visual scenes under the tight computational constraints required by real-time solutions on embedded devices. The Embedded Vision Workshop will provide a venue for discussing these challenges by bringing together researchers and practitioners from the different fields outlined above.
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Within deep learning, Euclidean geometry is the default basis for deep neural networks, yet the naive assumption that such a topology is optimal for all data types and tasks does not necessarily hold. A growing body of evidence suggests that data and the representations we aim to learn can be better captured through learning in corresponding geometries that exhibit non-Euclidean structures. Interest in non-Euclidean deep learning has grown dramatically in recent years, driven by advancing methodologies, libraries, and applications. The 2nd Beyond Euclidean workshop brings together computer vision researchers and keynote speakers who share an interest in exploring non-Euclidean geometry.
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The Multispectral Imaging for Robotics and Automation (MIRA) workshop brings together researchers and practitioners at the intersection of multispectral imaging, computer vision, and robotics. By leveraging data beyond the visible spectrum, multispectral imaging enables robust perception in challenging conditions, supporting applications from autonomous driving and industrial inspection to agricultural automation and search and rescue. MIRA aims to foster interdisciplinary collaboration across academia and industry, highlighting advances in sensor technology, spectral image processing, and downstream tasks like detection, segmentation, and decision-making. We welcome contributions exploring novel methods, applications, and datasets that advance the state of multispectral robotics.
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This workshop is organized as a collaboration between the 6th Workshop on Computer Vision for Analysis of Underwater Imagery (CVAUI) and the 3rd Automated Analysis of Marine Visual Data for Environmental Monitoring (AAMVEM). Visually monitoring marine environments poses a vastly different task compared to monitoring terrestrial environments. It is physically challenging to acquire underwater data, and the data typically have low signal-to-noise ratios due to the scattering nature of the water body. The aim of this workshop is to deepen the understanding of the challenges related to marine monitoring and to advance computer vision techniques to address them.
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Humans use social intelligence to interpret and navigate interactions with other people and agents in our shared world. As AI systems become pervasive in human social situations, it is crucial to improve the social intelligence of these systems in order for them to seamlessly work with, for, and around humans. This workshop aims to bring together researchers from computer vision and other communities to collaborate towards building computational foundations for core social intelligence abilities. This edition of our workshop centers discussions, keynotes, and paper presentations around the topics of reasoning, multimodality, and embodiment in socially-intelligent AI.
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This workshop explores how multi-modal foundation models can revolutionize cancer care by integrating AI, computer vision, and machine learning. By leveraging diverse data types—such as medical imaging, genomics, and EHRs—these models enable earlier detection, personalized treatment, and better outcome prediction. Pre-trained on large datasets and fine-tuned for specific tasks, they offer adaptability across cancer types and clinical settings. The event brings together experts from academia, industry, and healthcare to share research, tackle challenges in data integration and model interpretability, and promote clinical translation. The goal is to advance cancer research and accelerate the real-world impact of AI in oncology.
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In recent years, advances in machine learning and computer vision have driven continual learning (CL), allowing models to learn new tasks incrementally while retaining prior knowledge without full retraining. Early CL focused on unimodal data like images for classification, but powerful multimodal models now unify images, videos, text, and audio. Multimodal continual learning (MCL) must tackle unique challenges, including modality-specific forgetting, imbalance, and maintaining cross-modal links. This MCL workshop will address these issues, highlight new research directions, and promote collaboration among researchers, practitioners, and industry, advancing inclusive, efficient continual learning for modern AI systems.
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This workshop aims to advance the frontier of multimodal AI systems that can effectively reason across specialized domains requiring extensive domain knowledge. Recent advancements in multimodal AI—combining information from text, images, audio, and structured data—have unlocked impressive capabilities in general-purpose reasoning. However, significant challenges persist when these systems encounter scenarios demanding deep domain expertise in fields such as medicine, engineering, and scientific research. Such contexts require expert-level perception and reasoning grounded in extensive subject knowledge, highlighting the need for specialized strategies to handle domain-specific complexity. Through invited talks, panel discussions, and interactive poster sessions, researchers and practitioners from diverse backgrounds will share the latest developments, ongoing hurdles, and promising future directions for knowledge-intensive multimodal reasoning. The workshop aims to foster collaboration and stimulate innovation towards the development of next-generation multimodal AI systems capable of reliable, transparent, and contextually grounded reasoning in specialized, high-stakes environments.
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Depth and pose estimation are critical for enabling machines to interact effectively with the real world. Depth estimation provides the spatial structure of a scene, pose estimation localises and orients objects within it, both fundamental for robotics, augmented reality, and 3D understanding. Traditional approaches achieved impressive results on standard benchmarks like KITTI and Middlebury. However, when these methods encounter reflective and transparent objects, their performance degrades significantly. This limitation is particularly problematic as these challenging materials are common in everyday environments. TRICKY 2025 features two complementary challenges encouraging the development of next-generation algorithms capable of advanced reasoning on non-Lambertian objects.
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End-to-End 3D Learning (E2E3D) investigates unified, fully differentiable frameworks to map raw sensor data into comprehensive 3D representations. By merging multiple handcrafted stages into a single trainable pipeline, E2E3D strives to scale spatial understanding. Topics include self-supervised pretraining of large-scale 3D foundation models, efficient real-time inference on resource-limited platforms, and automated, high-fidelity 3D annotation methods. We showcase applications in autonomous driving, robotics, AR/VR, and scientific imaging—demonstrating how integrated 3D systems enhance perception, content generation, and science. Through cross-disciplinary talks, posters, and panels, participants will help define the next generation of robust, real-world 3D AI.
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The R6D workshop discusses topics related to model-based and model-free 6D object pose estimation which are relevant for applications such as robotic manipulation and augmented reality. The 10th workshop edition is organized in conjunction with the BOP Challenge 2025 that benchmarks the latest pose estimation methods in challenging settings including the new BOP-Industrial datasets. Find out about the latest trends and remaining challenges in object-centric 3D vision and learn how the latest methods perform in the wild on real robots.
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The workshop on Graphic Design Understanding and Generation (GDUG) aims to bring together researchers, creators, and practitioners to discuss the important concepts, technical perspectives, limitations, and ethical considerations surrounding recognition and generative approaches to graphic design and documents. While recent advances in generative AI are making impressive strides in creative domains, there is a disconnect between research attempts and the real-world workflow that involves graphics design, such as the creation of a website, posters, online advertisements, social media posts, infographics, or presentation slides, where creators do not paint pixels but instead work with structured documents, such as layered object representation, stylistic attributes, and typography.
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Over the past two decades, SLAM (Simultaneous Localization and Mapping) has evolved significantly, transitioning from traditional methods to deep learning and, more recently, to Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS). Since 2021, a surge of over 200 papers has reshaped the field, enabling new applications like realistic novel view synthesis. However, this rapid progress also raises challenges, such as lack of standardized benchmarks and understanding of key design choices. This workshop aims to unite researchers interested in dense neural SLAM, fostering discussion through keynotes, posters, and panels to explore emerging trends and future directions.
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Recent advances in computer vision have significantly impacted the retail sector, introducing new opportunities and challenges across both physical and online domains. This workshop explores key problems such as shopper-product interaction, fine-grained recognition of visually similar and frequently changing products, and large-scale visual search across over 100,000 product classes. It also showcases advancements in generative models for tasks like product image synthesis and virtual try-on. These are just some of the challenges in the retail domain. By highlighting recent progress and open research directions, the workshop aims to bring together researchers and practitioners to advance the state of computer vision in retail.
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Comics are a uniquely compelling visual storytelling medium, blending images and text, but they present significant challenges for Artificial Intelligence. Unlike natural images, comics rely on abstract, stylized panels and implicit transitions that demand complex inference, causing even state-of-the-art vision-language models to struggle with tasks like panel sequencing and cross-panel reasoning. This workshop brings together researchers from computer vision, cognitive science, and multimedia analysis to advance AI-driven comic understanding. Through talks and discussions, we will explore new methodologies for multimodal reasoning.
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Short-form videos (SVs) have proliferated as primary sources for entertainment, information, advertising, and social communication. Marketers are increasingly turning to SVs to reach their customers, and creative artists have begun to view SVs as a separate form of art and media for designing their content. Currently, SVs account for 90% of internet traffic and are estimated to be about 2.5 times more engaging than longer videos, driving their widespread popularity and diversity. Our workshop aims to consolidate efforts in SV understanding, highlight specific challenges, map the research landscape, and establish a foundation for future development in this rapidly expanding domain.
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Face Anti-Spoofing (FAS) has become an important part of ensuring the reliability of biometric authentication systems. However, achieving unified detection of physical and digital attacks remains a serious challenge. Physical presentation attacks often introduce artifacts such as color distortion and moiré, while digital forgeries often tamper with facial images at the pixel level in an imperceptible way. To advance the development of this field, we released a massively expanded dataset, UniAttackData+, at the 6th Face Anti-Spoofing Workshop (ICCV 2025). The dataset covers 2,875 participants from three different ethnic groups (Africa, East Asia, and Central Asia), and a total of 18,250 real videos were collected under various lighting, background, and acquisition device conditions. For each participant, we designed and applied 54 attack methods (including 14 physical attacks and 40 digital attacks), generating a total of 679,097 forged videos, providing a rich, diverse, and challenging data resource for unified attack detection.
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The 2nd Workshop and Challenge on Unlearning and Model Editing (U&ME) is a half-day event at ICCV 2025 in Hawaii on October 19, 2025, in the afternoon, and focuses on the growing need for new, efficient, and effective techniques for editing trained models, especially large generative models. Such models have practically unlimited functionality in the output they can generate. To provide this functionality, generative models require massive amounts of data and enormous compute costs to train, making it prohibitively expensive to retrain them whenever the need arises: when safety risks are uncovered, when deploying them to compute or storage-restricted platforms, or simply due to changing requirements. In particular, ensuring these models are safe and compliant with regulations can be difficult due to their broad range of capabilities and a continuously evolving regulatory landscape.
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The workshop brings together researchers, practitioners, and policy‑makers to advance the state‑of‑the‑art in applying artificial intelligence to Earth observation for sustainability challenges. Technically, this workshop explores how state-of-the-art EO data-tailored foundation models, efficient architectures, and novel learning paradigms can be leveraged or adapted to tackle pressing sustainability challenges. Topics include, but are not limited to, climate monitoring, disaster response, biodiversity, agriculture, urban development, clean energy, and social economics.
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This workshop explores efficient methodologies in visual computing, focusing on data-efficient techniques (e.g., image/video compression), label-efficient strategies (e.g., zero/few-shot learning), and model-efficient approaches (e.g., sparsification, quantization). By bringing together experts in these areas, we aim to foster the exchange of recent findings and discuss future directions. Given the growing importance of efficiency in practical deployments, this topic has attracted significant research interest. The workshop provides a platform for presenting novel perspectives and addressing core challenges in visual computing, ultimately driving advancements that bridge academic research with real-world applications.
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Modern vision and multimodal models depend on massive datasets and heavy compute, magnifying costs, energy use, bias, copyright, and privacy risks. The “DeepSeek shock” of January 2025 spotlighted the urgency of learning powerful representations under tight resource limits. Now in its third edition, our workshop continues to explore strategies for robust representation learning when data, labels, modalities, parameters, or compute are scarce. We focus on techniques such as synthetic and distilled data, self-supervision, transfer learning, sparsity, and low-rank adaptation that squeeze maximum performance from minimal resources.
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Women in Computer Vision Workshop (WiCV@ICCV 2025) aims to promote and increase the participation of female-identifying researchers in the computer vision community. The workshop features technical talks, poster sessions, a panel discussion, and a mentoring dinner to foster networking, visibility, and collaboration. WiCV provides a platform to present cutting-edge research, share career insights, and discuss challenges faced by women in CV. The event is open to all ICCV attendees and strongly encourages junior researchers and students to participate. Through community support and industry sponsorship, WiCV continues its mission to build a more inclusive and diverse research ecosystem.
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Designing systems with humans in the loop to assist users is an active research area with potential societal impact. Investigations require many innovations, tools, and evaluation criteria, even compared to fully autonomous systems. Implementing such systems demands significant effort to achieve reliability and raises issues related to usability, privacy, and acceptability. Moreover, multidisciplinary competencies are needed to adapt algorithms to industrial, social, medical, and economic constraints. The goal is to provide a view of how recent findings in computer vision and robotics are changing assistive technologies, emphasizing related issues and how researchers in various fields have addressed them.
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The Visual Quality Assessment Competition (VQualA) Workshop at ICCV 2025 aims to advance perceptual quality evaluation in computer vision by addressing the limitations of traditional metrics such as PSNR and SSIM. Leveraging deep learning, generative models, and multimodal large language models (MLLMs), the workshop emphasizes human-aligned assessments. It features seven diverse challenges spanning low-level vision, document enhancement, face image quality, AIGC video evaluation, and visual comparison via MLLMs. Through both scalar metrics and comparative reasoning tasks, VQualA fosters more interpretable, robust, and perceptually meaningful evaluation. It unites academic and industrial communities to push the frontier of visual quality assessment forward.
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The ability to perceive, understand, and interact with 3D scenes is crucial for applications in AR/VR, robotics, healthcare, and beyond. Current 3D scene understanding models are largely limited to low-level recognition tasks such as object detection or semantic segmentation, and struggle to generalize beyond predefined training labels. Recently, large VLMs such as LLAVA have demonstrated impressive capabilities. Initial works have shown their potential to extend 3D scene understanding not only to open vocabulary recognition, but also reasoning about affordances, activities, and properties of unseen environments. This workshop aims to define tasks, metrics, and benchmarks to advance this emerging direction.
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Imagine a world where computer vision-based systems can analyze a video of an athlete, a surgeon, a patient, or a factory worker and instantly provide expert-level actionable feedback---correcting techniques, identifying inefficiencies, and helping people refine their skills in real time. Thanks to rapid progress in video understanding, this vision is becoming reality. AI-powered systems can now analyze complex human activities, assess performance, and generate intelligent feedback, unlocking new possibilities in sports, healthcare, manufacturing, education, rehabilitation, and beyond. Through Expert Keynotes and Invited Contributions, this workshop will explore the cutting edge of skilled activity understanding, assessment, and feedback generation, bridging research and real-world applications.
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This workshop explores the intersection of Computer Vision and NLP, focusing on joint vision-language understanding. Recent advances, particularly in large-scale multimodal pretraining with transformers, have driven progress in various tasks. Topics include visual-linguistic representation learning, VQA, captioning, visual dialog, referring expressions, vision-and-language navigation, embodied QA, and text-to-image generation. We emphasize joint video-language understanding due to its unique challenges. Additionally, we welcome critical work on dataset and algorithmic bias, generalization issues, and efforts toward transparency and explainability.
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The workshop will explore cutting-edge computer vision applications in digital advertising and marketing, covering fundamental visual understanding tasks, marketing optimization systems, brand intelligence, responsible AI practices, creative generation techniques, and emerging technologies that are transforming how brands connect with audiences through visual content. Key focus areas include multimodal data processing, visual similarity analysis, real-time bidding optimization, dynamic creative optimization, brand safety monitoring, and privacy-preserving analytics. The program will also address generative AI applications in advertising, automated visual optimization, and personalized content creation, while emphasizing ethical considerations and bias mitigation in marketing technology.
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The 2nd Workshop on Distillation of Foundation Models for Autonomous Driving (WDFM-AD) focuses on advancing the state of the art in deploying large foundation models—such as vision-language models (VLMs) and generative AI (GenAI) models—into autonomous vehicles through efficient distillation techniques. Building on the success of our previous workshops on large language and vision models for autonomous driving, WDFM-AD aims to bring together researchers and industry professionals to explore innovative approaches that accelerate the safe, efficient, and scalable adoption of cutting-edge AI technologies in autonomous vehicles.
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The ninth AI City Challenge advanced real-world AI applications in transportation, automation, and safety, attracting 245 teams from 15 countries—a 17% increase. Featuring four tracks, the 2025 edition introduced challenges in 3D multi-camera tracking, traffic video question answering, warehouse spatial reasoning, and efficient fisheye camera detection. Tracks 1 and 3 utilized synthetic data from NVIDIA Omniverse. The evaluation platform ensured fair benchmarking with submission limits and held-out test sets. Public dataset releases reached over 30,000 downloads. Final rankings, announced post-competition, highlighted strong global participation and new benchmarks across multiple tasks, driving progress in intelligent visual perception and reasoning.
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This workshop explores a holistic approach to ego-exo sensing, integrating vehicle sensors, roadside cameras, aerial imagery, and V2V communications to advance transportation intelligence and drive progress toward smart mobility. We examine how ego-exo sensing networks enhance safety-critical scenario detection and generation, comprehensive environmental perception, cooperative driving, and multi-agent decision making, among other crucial tasks shaping the future of mobility. This workshop bridges siloed research efforts to create unified approaches for heterogeneous sensor fusion that will define next-generation mobility systems.
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The GAIA (Generative AI for Biomedical Image Analysis) workshop at ICCV 2025 explores how generative AI is transforming medical imaging and healthcare. The workshop focuses on three key areas: (1) Data synthesis and clinical modeling using generative models for anatomically accurate image creation and disease simulation, (2) Multimodal learning that integrates visual data with medical reports through large language models, and (3) Workflow automation streamlining medical imaging from acquisition to diagnosis. Bringing together experts from computer vision, healthcare, and AI research, the workshop addresses challenges in interpretability, regulatory compliance, and clinical reliability while showcasing opportunities for interdisciplinary collaboration in advancing biomedical image analysis.
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The VLM3D workshop brings together pioneers in vision-language modeling and 3D medical imaging to tackle the limitations of current models, which remain primitive and clinically unfit for real-world deployment. Through keynotes, discussions, and a dedicated benchmark challenge, we will explore why today’s AI struggles with the complexity of 3D data and how to advance towards robust, deployable solutions. We aim to bridge the gap between research and clinical practice by defining critical next steps for generating reliable, interpretable, and clinically useful models. Join us to help shape the future of AI-driven 3D medical imaging.
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This workshop explores cutting-edge technologies, focusing on AI and computer vision, to advance autonomous, efficient, and patient-centered healthcare. It addresses challenges like medical errors and the need for precise diagnostics amid rapid technological advancements. The event facilitates collaboration among AI researchers, clinicians, and industry professionals through invited talks and paper presentations. These cover theoretical and practical applications of visual perception technologies to enhance workflow efficiency, diagnostic accuracy, reduce errors, and improve patient care. By fostering partnerships, the workshop tackles issues like staff shortages and rising healthcare costs, promoting innovative solutions for a more effective healthcare system.
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The Workshop on Cultural Continuity of Artists (WCCA) brings together researchers, creators, and cultural institutions to explore how computer vision, multimodal AI, and XR technologies can safeguard and reinterpret artistic legacies. Our inaugural edition, co‑located with ICCV 2025, highlights the visionary South Korean fashion designer André Kim and introduces a rich, newly curated dataset from his archives.
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Generative AI excels at producing stunning visuals but often fails at creating coherent, engaging stories. Storytelling demands consistency in character, plot, and setting—areas where current models fall short. This workshop explores combining advanced visual models, large language models, and multi-modal AI to generate narratives that are visually consistent and compelling. Our goal is to push generative AI beyond impressive graphics, enabling it to deliver dynamic, cohesive stories and broaden its role in content creation.
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The 1st Multimodal Sign Language Recognition Workshop (MSLR 2025) convenes researchers to explore vision, sensor, and generative based approaches. Emphasizing multimodal fusion of RGB video, depth maps, skeletal and facial keypoints, and radar data, MSLR showcases systems optimized for real-world variability and privacy. Topics include statistical and neural sign-to-text/text-to-sign translation, cross-lingual methods, multilingual support, advanced generative synthesis, and inclusive dataset creation. Through keynotes, presentations, and challenges on continuous and isolated sign language recognition tasks, attendees will engage with novel benchmarks, metrics, and ethical data curation. Focus areas also encompass privacy-preserving sensing and healthcare accessibility. All backgrounds are welcome to contribute.
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AMFG 2025 invites cutting-edge work in face, gesture, and multimodal recognition, where deep learning has unlocked unprecedented gains—but also raised concerns around generalization, transparency, and robustness. As models saturate benchmarks yet falter in real-world scenes with occlusion, motion, or lighting shifts, new challenges demand innovative solutions. Topics include detection and tracking, neural rendering, generative modeling, vision-language systems, kinship and soft biometrics, cross-modal fusion, benchmark creation, and ethical AI. With applications spanning HCI, surveillance, AR/VR, and behavioral science, AMFG aims to push beyond recognition into systems that interpret, adapt, and interact. Submit your work and shape the future of embodied vision.
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With growing global security concerns, biometric-based authentication and identification have become indispensable due to their reliability and robustness. Beyond physical biometrics, behavior understanding is emerging as a critical domain, aiming to interpret complex behavioral patterns that arise during interactions. Integrating both biometric and behavioral insights can lead to more secure, adaptive, and context-aware identity verification systems. Computer vision plays a pivotal role in analyzing and synthesizing biometric, identity, and behavior data. Recent advancements in research, driven by deep learning and multimodal analysis, have significantly expanded the field. However, numerous challenges remain, including effective joint modeling of multimodal cues occurring at different time scales, handling the inherent uncertainty of machine-detectable behavioral evidence, and addressing long-term dependencies in human behavior and identity recognition. This workshop aims to bring together leading researchers, industry experts, and government agencies to discuss the latest breakthroughs. It will serve as a platform to explore cutting-edge solutions, share innovative methodologies, and address the open challenges in this evolving field.
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The ICCV 2025 Workshop on Foundation and Generative Models in Biometrics aims to bring together researchers to discuss state-of-the-art advancements, applications, and challenges in using foundation and generative models for biometric recognition, analysis, and security. While foundation models have gained significant attention in recent years, their applications in biometrics remain relatively underexplored. This workshop seeks to encourage discussions that inspire innovation and address the challenges of applying these advanced models in real-world biometric systems. The program will feature invited talks and paper presentations.
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Generating realistic 3D content has been a long-standing problem and graphics, which has recently attracted increasing attention. This workshop aims to bring together researchers to explore recent advances and future directions toward building fully controllable 3D content generation pipelines. We focus on four key aspects: (1) Representations suitable for generating high-quality and controllable 3D assets; (2) Modeling techniques that enable scalable, diverse, and photorealistic generation of humans, objects, and scenes; (3) Interaction modeling for capturing dynamic human-object relations with physical realism; (4) Applications of 3D content creation in areas such as embodied AI, construction and digital design.
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Despite recent advances in 3D modeling, reconstruction, and generation, many methods remain limited to static scenes or dense viewpoints, making them less effective in real-world, dynamic, and often sparse or noisy settings. This workshop aims to gather researchers and practitioners focused on modeling, reconstructing, and generating dynamic 3D objects or scenes under challenging, in-the-wild conditions. Leveraging progress in 3D learning, the abundance of 2D/3D data, and powerful generative models, now is an opportune time to make 3D vision more robust and accessible. The workshop encourages contributions from standard 3D topics and broader 4D directions involving dynamics and video generation.
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Personalization in Generative AI Workshop (P13N) is a full-day workshop that brings together leading researchers and industry experts to explore cutting-edge personalization techniques in generative AI. The event will feature paper presentations, panel discussions, and a competition focusing on personalized generative models across images, and videos. Topics include advanced optimization methods for personalizing diffusion models, multi-subject composition, cross-modal personalization, AR/VR personalization, dataset curation and benchmarking, as well as ethical and privacy considerations.
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The intersection of foundation models and multimodal learning is a significant and widely discussed topic that complements the main ICCV conference. This workshop aims to encourage an interdisciplinary discussion on recent advancements, ongoing challenges, and future directions in multimodal foundation models, which have achieved breakthroughs by applying techniques across computer vision, natural language, and robotics.
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The CV4A11y Workshop focuses on how new advances in vision foundation models and generative AI can help improve accessibility for people with disabilities. These technologies have great potential, but there are still important challenges, such as bias, limited data, lack of explainability, and real-world deployment issues. This workshop brings together experts in computer vision, AI, human-computer interaction, and accessibility to share new ideas, discuss open problems, and explore future directions. Our goal is to support the development of AI-powered tools that are more inclusive, useful, and effective in improving daily life for individuals with disabilities.
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The Workshop on Continual Learning in Computer Vision (CLVision) aims to gather researchers and engineers from academia and industry to discuss the latest advances in Continual Learning. In this workshop, there will be regular paper presentations, invited speakers, and technical benchmark challenges to present the current state of the art, as well as the limitations and future directions for Continual Learning, arguably one of the most crucial milestones of AI.
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This workshop aims to bridge the gap between computer vision and large language/reasoning models, focusing on complex tasks requiring advanced reasoning capabilities. We will explore how models can comprehend complex relationships through slow-thinking approaches like Neuro-Symbolic reasoning, Chain-of-Thought, and Multi-step Reasoning, pushing beyond traditional fixed tasks to understand object interactions within complex scenes. The goal is to bring together perspectives from computer vision, multimodal learning, and large language models to address outstanding challenges in multimodal reasoning and slow thinking in the context of large reasoning models, fostering more flexible and robust understanding in AI systems.
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AI agents powered by Large Language Models (LLMs) have shown strong reasoning abilities across tasks like coding and research. With the rise of Multimodal Foundation Models (MFMs), agents can now integrate visual, textual, and auditory inputs for richer perception and decision-making. This workshop explores the development of Multimodal AI Agents across four categories: Digital, Virtual, Wearable, and Physical. We will discuss their applications in science, robotics, and human-computer interaction, as well as key challenges in cross-modal integration, real-time responsiveness, and interpretability. The goal is to advance robust, context-aware agents for complex, real-world environments.
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The 7th LSVOS Workshop focuses on advancing research in Video Object Segmentation (VOS) and Referring Video Object Segmentation (RVOS). For VOS, we set two tracks, one is based on Complex Video Object Segmentation (MOSEv2) dataset, the other is based on the MOSEv1 and LVOS datasets, targeting long-term videos and complex real-world scenes with challenges like object disappearance and reappearance, inconspicuous small objects, heavy occlusions, and crowded environments. The RVOS track continues with the MeViS dataset, which emphasizes motion-based language expressions and demands fine-grained temporal reasoning. In addition to the challenges, the workshop hosts invited talks from leading researchers, covering topics such as vision-and-language, motion understanding, cognitive modeling, and embodied intelligence in video understanding.
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The workshop will focus on physical reliability and effective interactivity in world models for applications requiring precise physical reasoning and dense environmental interactions, such as robotics, autonomous systems, and multi-agent interactions. Beyond generating realistic predictions, world models must enforce physical consistency through differentiable physics, hybrid modeling, and adaptive simulation techniques. By bringing together researchers from machine learning, computer graphics, and physics-based modeling, the workshop will explore classical and cutting-edge approaches to aligning world models with real-world physics and extending them beyond simulation.
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The 1st Embodied Spatial Reasoning Workshop at ICCV 2025 explores the integration of spatial understanding in intelligent agents. Focus areas include Embodied AI, which allows agents to perceive, reason, and act in real-world or simulated environments, and Spatial Reasoning, which involves interpreting spatial relations and sensory feedback. The workshop also delves into the development of Embodied World Models for building spatially coherent, semantically grounded internal representations, and Robot Spatial Reasoning, addressing challenges in planning and acting under uncertainty and task constraints. The goal is to advance robust, generalizable spatial reasoning for embodied agents.
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Neuromorphic vision sensors, or event cameras, mimic biological vision by asynchronously detecting changes in illumination, enabling high temporal resolution, low power consumption, and no motion blur. These unique features support advanced applications in robotics, autonomous vehicles, and human behavior analysis, especially for motion-centric tasks. Event cameras excel in low-light conditions and fast dynamics, enabling real-time obstacle avoidance, emotion recognition, defect detection, and more. Their microsecond latency and high dynamic range offer significant advantages over conventional cameras. Moreover, their inherent data sparsity contributes to privacy preservation.
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Image manipulation, restoration, and enhancement are key computer vision tasks serving as an important frontend of further tasks. Each step forward eases the use of images by people or computers. Not surprisingly then, there is an ever growing range of applications in fields such as surveillance, automotive, etc. or for mobile and wearable devices. 6th AIM workshop provides an overview of the advances in those areas, an opportunity for academic and industrial attendees to interact and explore collaborations. 32 papers and 18 associated competitions are gauging state-of-the-art on topics such as super-resolution, denoising, deblurring, ISP, segmentation, efficient models or quality assessment.
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Computer vision and machine learning are critical tools to support large-scale materials characterization and development of new materials. Quantified structure features that are extracted from the data can be leveraged in statistical and machine learning models that establish processing-structure-property-performance (PSPP) relationships to identify non-linear and unintuitive trends in the high dimensional materials development space further accelerating materials development. The aim of workshop is to bring together cross-disciplinary researchers to demonstrate recent advancements in machine learning, computer vision, and materials microscopy, and discuss open problems such as representation learning, uncertainty quantification, and explainability in materials microscopy analysis.
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The ICCV 2025 Workshop on Curated Data for Efficient Learning (CDEL) seeks to advance the understanding and development of data-centric techniques that improve the efficiency of training large-scale machine learning models. As model sizes continue to grow and data requirements scale accordingly, this workshop brings attention to the increasingly critical role of data quality, selection, and synthesis in achieving high model performance with reduced computational cost. Rather than focusing on ever-larger datasets and models, CDEL emphasizes the curation and distillation of high-value data—leveraging techniques such as dataset distillation, data pruning, synthetic data generation, and sampling optimization. These approaches aim to reduce redundancy, improve generalization, and enable learning in data-scarce regimes. The workshop will bring together researchers and practitioners from vision, language, and multimodal learning to share insights and foster collaborations around efficient, scalable, and sustainable data-driven machine learning.
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In this workshop, we focus on 3D models enriched with processes and semantic connections, similar to those in computer game and robotic environments. These models can range in fidelity from simplified 3D representations (Digital Cousins) to highly accurate reconstructions of real-world counterparts (Digital Twins). 3D Gaussian Splatting and Diffusion Models, have demonstrated impressive success in generating 3D representations from images and video. The next frontier in 3D representation is enriching models by integrating both physical and semantic object properties through generative AI and retrieval-based approaches.
Digital Twin Generation from Visual Data: A Survey
https://arxiv.org/abs/2504.13159
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Despite the unprecedented surge, the presence of biases within AI models is a critical concern, perpetuating disparities and ethical dilemmas. In the last years, the scientific community has increasingly focused on understanding and addressing model bias, as evidenced by a significant uptick in research across various disciplines. In this context, we present the second edition of the workshop FAILED. This initiative aims to convene experts and practitioners from diverse backgrounds to explore innovative strategies for rectifying biases and promoting fairness and transparency in AI systems. Join us in this collaborative endeavor and be a part of this transformative journey!
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Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of Mobile Intelligent Photography and Imaging (MIPI). The workshop's main focus is on MIPI, emphasizing the integration of novel image sensors and imaging algorithms.
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The 3rd edition of the Workshop seeks to explore novel directions for making deep learning models more efficient. We'll delve into low-bit quantization, a technique that significantly reduces model size and computational demand by representing model weights and activations with fewer bits. This is crucial for deploying models on-device, especially given the ever-growing model size. A core focus of the workshop will be to study ways of maintaining accuracy under extreme quantization, with recent breakthroughs demonstrating exciting potential for achieving this. Hear about the latest trends from our invited speakers and presented papers.
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The rapid advancement of foundation models in fields like healthcare, cybersecurity, and finance highlights the urgent need to improve their anomaly detection capabilities. Despite their growing application in high-stakes areas, the challenges of using these models for anomaly detection remain underexplored. The Anomaly Detection with Foundation Models (ADFM 2025) workshop aims to address this gap by focusing on the intersection of foundation models and anomaly detection. Our organizing and technical committee, composed of leading experts, provides a platform for advancing research and discussing the recent breakthroughs, and the technical and ethical implications of deploying these models. ADFM 2025 will foster interdisciplinary collaboration and contribute to the development of more reliable and effective anomaly detection systems in artificial intelligence.
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As large-scale 3D scene modeling becomes increasingly important for applications such as urban planning, robotics, autonomous navigation, and virtual simulations, the need for diverse, high-quality visual data is greater than ever. However, acquiring dense and high-resolution ground-level imagery at scale is often impractical due to access limitations, cost, and environmental variability. In contrast, aerial and satellite imagery provide broader spatial coverage but lack the fine-grained details needed for many downstream applications. Combining images from multiple altitudes — from ground cameras to aerial drones and satellites—offers a promising solution to overcome these limitations, enabling richer, more complete 3D reconstructions. How can we achieve coherent and accurate 3D scene modeling when our visual world is captured from vastly different altitudes—ground, aerial, and satellite—under varying conditions? Each altitude offers distinct advantages, but cross-altitude data fusion introduces significant challenges: sparse and incomplete views, visual ambiguities, spatio-temporal inconsistencies, image quality variations, dynamic scene changes, and environmental factors that alter topology over time. Traditional 3D reconstruction methods, optimized for dense and structured inputs, struggle with such heterogeneous multi-altitude data. Advances in multi-scale feature alignment, neural scene representations, and robust cross-view fusion offer promising solutions, but key challenges remain.
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Multimodal representation learning is central to modern AI, enabling applications across retrieval, generation, RAG, reasoning, agentic AI, and embodied intelligence. With the growing ubiquity of multimodal data—from e-commerce listings to social media and video content—new challenges arise in multimodal retrieval, where both queries and indexed content span multiple modalities. This task requires deeper semantic understanding and reasoning, especially at scale, where data complexity and noise become significant hurdles. The half-day event will feature keynote talks, oral and poster presentations.
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The goal of the Human-inspired Computer Vision workshop is to link and disseminate parallel findings in the fields of neuroscience, psychology, cognitive science, and computer vision, to inform the development of human-inspired computational models capable of solving visual tasks in a human-like fashion. Although the high performance reached by recent computer vision approaches, the relationship between machine and human vision remains unclear. Investigating such a relationship is timely and important both to improve machine vision, by identifying and tackling gaps between humans and machines, and to understand/enhance human vision, by developing interpretable models useful to explain neuroscientific and cognitive observations.
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The first Workshop & Competition on Computationally Optimal Gaussian Splatting (COGS) welcomes researchers working on techniques for efficient 3D Gaussian Splatting (3DGS). While 3DGS has advanced rapidly, real-time rendering of Gaussian splats on resource-limited devices (e.g., smartphones, AR/VR headsets) remains a challenge. COGS aims to lower the barrier to entry and encourage new research in this area. The event will feature keynote talks from leading experts and a panel session exploring the current state and future directions of this promising technique.
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This workshop focuses on generative scene completion, which is indispensable for world models, VR/AR, telepresence, autonomous driving, and robotics. It explores how generative models can help reconstruct photorealistic 3D environments from sparse or partial input data by filling in occluded or unseen spaces. Topics include world models, generative models, inpainting, artifact removal, uncertainty, controllability, and handling of casual data. We will discuss how related directions like text-to-3D and single-image-to-3D compare with scene completion, where more input constraints must be satisfied. The workshop highlights key challenges and recent progress in transforming incomplete real-world captures into immersive environments. https://scenecomp.github.io/
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This workshop introduces the concept of a findings-style track to the computer vision community. NLP conferences have included Findings since 2020 to publish technically sound work, but which may not meet the main conference's threshold for novelty, impact, or excitement. There are many important results the community should be made aware of, and this venue provides an audience without delays for further submission iterations, or that might otherwise be lost if never published. This workshop provides a vehicle to discuss creating a computer vision Findings track and present Findings-quality papers to demonstrate their impact and benefits to inform future conferences.
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Bio-image computing (BIC) is a rapidly growing field at the interface of engineering, biology and computer science. Advanced light microscopy can deliver 2D and 3D image sequences of living cells with unprecedented image quality and ever increasing resolution in space and time. The emergence of novel and diverse microscopy modalities has provided biologists with unprecedented means to explore cellular mechanisms, embryogenesis, and neural development, to mention only a few fundamental biological questions. The enormous size and complexity of these data sets, which can exceed multiple TB per volume or video, requires state-of-the-art computer vision methods.
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The Computer Vision for Ecology workshop aims to bring together experts to foster discussion on the automation of ecological data collection, collation, and analysis. The goal is to establish a hub for the broader computer vision and ecology community at ICCV. The workshop encompasses applications of computer vision across a wide variety of ecological systems, spanning both terrestrial and aquatic systems, diverse geographic regions, and urban to wildland settings. The topics we aim to address include, but are not limited to, remote sensing, bioacoustics, video and image-based monitoring, citizen science, long-tailed recognition, zero-shot learning, expert AI systems, and model deployment.
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Welcome to the 2nd Workshop on AI for Content Generation, Quality Enhancement and Streaming. This workshop focuses on unifying new streaming technologies, computer graphics, and computer vision, from the modern deep learning point of view. Streaming is a huge industry where hundreds of millions of users demand everyday high-quality content on different platforms. Computer vision and deep learning have emerged as revolutionary forces for rendering content, image and video compression, enhancement, and quality assessment. From neural codecs for efficient compression to deep learning-based video enhancement and quality assessment, these advanced techniques are setting new standards for streaming quality and efficiency. Moreover, novel neural representations also pose new challenges and opportunities in rendering streamable content, and allowing to redefine computer graphics pipelines and visual content.
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This workshop addresses the critical problem of category-level object pose estimation and its applications within complex robotic manipulation scenarios. Pose estimation, a fundamental challenge in both 3D computer vision and robotics perception, involves accurately determining an object's complete 6-degree-of-freedom (6DoF) pose, comprising its 3D rotation and translation. Our workshop specifically focuses on advancing category-level pose estimation methods under realistic and demanding robotic manipulation settings, particularly emphasizing articulated objects, dynamic environments with potential human-object interactions, and objects subject to severe occlusions and partial visibility.
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Recent advancements in image editing applications such as relighting, compositing, harmonization, and virtual object insertion have opened up new horizons in visual media, augmented reality, and virtual production---especially with the rise of powerful image generative models. However, evaluating the quality of results for these applications is still a significant challenge. Traditional image quality metrics are not always effective in capturing the perceptual realism and subtle effects these technologies aim to achieve. Additionally, relying on user studies can be time-consuming and introduces variability, making comparing methods consistently challenging. To address these issues, this workshop explores and develops standardized evaluation metrics to bridge the gap between quantitative assessment and qualitative perception.
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PHAROS-AFE-AIMI aims to present innovative approaches for predictive modeling using large medical image datasets, emphasizing deep learning models and transparent, human-centered integration of GenAI and LLMs in health services. It tackles key challenges at the intersection of computer vision and healthcare AI, including multidisease diagnosis, model explainability, fairness, domain adaptation, continual learning. With rising interest in trustworthy and interpretable AI, PHAROS-AFE-AIMI fosters discussion on responsible deployment in sensitive applications. PHAROS-AFE-AIMI is organised under PHAROS AI Factory, ensuring its topics having real-world relevance and strong foundation in cutting-edge research. Finally, the workshop includes two challenges (Multi-Source-Covid-19 Detection and Fair Disease Diagnosis).
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As imaging technologies advance, they surpass traditional capabilities, capturing and interpreting visual information beyond the limits of human perception. While these cutting-edge computational imaging systems push the boundaries of what can be seen and understood, they also quietly introduce critical ethical concerns related to privacy, safety, and robustness. Since these systems operate beyond human vision, many potential threats remain imperceptible, making them more difficult to detect and mitigate. This workshop aims to bring attention to these challenges and explore innovative solutions to deal with these challengings as imaging technologies push the boundaries of perceiving the invisible.
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In this workshop, we aim to shine a spotlight on this exciting yet underinvestigated field by prioritizing new approaches in audio-visual generation, as well as covering a wide range of topics related to audio-visual learning, where the convergence of auditory and visual signals unlocks a plethora of opportunities for advancing creativity, understanding, and also machine perception. We hope our workshop can bring together researchers, practitioners, and enthusiasts from diverse disciplines in both academia and industry to delve into the latest developments, challenges, and breakthroughs in audio-visual generation and learning.
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Intelligent robots are advancing rapidly, with embodied agents increasingly expected to work and live alongside humans in households, factories, hospitals, schools, etc. For these agents to operate safely, socially, and intelligently, they must effectively interact with humans and adapt to changing environments. Moreover, such interactions can transform human behavior and even reshape the environment—for example, through adjustments in human motion during robot-assisted handovers or the redesign of objects for improved robotic grasping. Beyond established research in human-human and human-scene interactions, vast opportunities remain in exploring human-robot-scene collaboration. This workshop will explore the integration of embodied agents into dynamic human-robot-scene interactions.
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Foundation models are revolutionizing the way we interact with AI—powering everything from search engines to scientific discovery. But as their reach expands, so do the risks. Can we truly trust these systems—before putting them to use? From AlexNet to LLaVA , the pace of innovation is staggering. Yet one thing remains constant: the urgent need for trustworthiness. In the foundation model era, we ask:What does trust mean at scale? Can classical insights still guide us? This workshop brings together researchers, engineers, and thought leaders to confront these challenges head-on. We’ll explore how to create models that are not just powerful, but robust, fair, interpretable, and accountable.
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The CVPPA aims to advance computer vision techniques for applications in plant phenotyping and agriculture to support sustainable food, feed, fiber, and plant-based fuel production. The workshop seeks to highlight unsolved challenges, showcase current methods, and expand the research community at the intersection of plant and computer sciences. Topics include segmentation, tracking, detection, and reconstruction in agricultural contexts, open-source tools, and annotated datasets with benchmarks. Effective plant phenotyping is urgently needed to support the sustainability of our planet and its inhabitants: having strong community structures and computer vision scientists enter this field is more crucial now than ever.
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Visual generative models have revolutionized our ability to generate realistic images, videos, and other visual content. However, with great power comes great responsibility. While the computer vision community continues to innovate with models trained on vast datasets to improve visual quality, questions regarding the adequacy of evaluation protocols arise. Automatic measures such as CLIPScore and FID may not fully capture human perception, while human evaluation methods are costly and lack reproducibility. Alongside technical considerations, critical concerns have been raised by artists and social scientists regarding the ethical, legal, and social implications of visual generative technologies. The democratization and accessibility of these technologies exacerbate issues such as privacy, copyright violations, and the perpetuation of social biases, necessitating urgent attention from our community. This interdisciplinary workshop aims to convene experts from computer vision, machine learning, social sciences, digital humanities, and other relevant fields. By fostering collaboration and dialogue, we seek to address the complex challenges associated with visual generative models and their evaluation, benchmarking, and auditing.
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EgoMotion, in its second edition, is a continuation workshop focusing on human motion modeling using egocentric, multi-modal data from wearable devices. We focus motion tracking, synthesis, and understanding algorithms from egocentric/exocentric cameras, non-visual sensors, and high-level derived data. The workshop also covers research that applies egocentric motion for character animation, simulation, robotic learning etc. In addition to algorithms, the workshop promotes recent open-source projects, research platforms, datasets and associated challenges to encourage and accelerate research in the field. We will include live demo sessions to encourage discussions.
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The Biometrics for Arts (ArtMetrics) workshop aims to explore the intersection of biometrics, computer vision and the arts to provide a more nuanced understanding of an artwork's provenance and maker(s). In the same way as Biometrics serves as a tool for person identification from unique phenotypic or behavioural traits, Biometrics for Arts aims at artist recognition from unique attributes detected on works of art, thus fostering dialogue between engineers, computer scientists, heritage scientists, conservators, and art historians. This workshop will showcase innovative applications of computer vision in the visual art domain, emphasizing the role of technology in supporting conservation practices and enhancing the management of museum and private collections. Key topics include pattern recognition in works of art, AI-driven artistic generation, AI-driven analysis of multimodal imaging data of works of art, and digital restoration of different media. ArtMetrics seeks to inspire interdisciplinary collaboration, highlighting how computer vision can both interpret and enhance the diverse world of art.
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In today’s rapidly digitalizing world, the ability to analyze documents automatically is becoming increasingly important in our daily life. Document Analysis plays a growing role in both industrial and cultural contexts, highlighting the need for AI systems capable of handling highly diverse documents, presenting significant challenges. This workshop seeks to address these issues by fostering interdisciplinary collaboration. By bringing together researchers and professionals from different domains, it aims to facilitate knowledge exchange, promote innovation, and advance the development of intelligent, adaptable solutions for Document Analysis in a wide range of applications.
Workshop
Abstract
As computer vision moves into real-world use, robust localization across diverse devices is crucial. The CroCoDL workshop unites experts in vision, robotics, and AR to tackle cross-device, multi-agent localization. Focusing on 3D vision, visual localization, embodied AI, and AR/VR/MR, it bridges academic research and real-world deployment. The inaugural event features invited talks, papers, and a competition. It also introduces CroCoDL, a new large-scale benchmark with synchronized data from phones, headsets, and robots. By connecting efforts in structure-from-motion, neural rendering, and embodied AI, the workshop advances scalable localization across domains, sensors, and dynamic environments.
Workshop
Abstract
Our workshop will feature insightful keynote talks and a panel discussion around multi-modal spatial intelligence. Key topics include enhancing MLLMs' reasoning with images and 3D data, advancing 2D/3D perception, and enabling embodied AI. We will also delve into dynamic physical world modeling and critically examine the trust, ethics, and societal impact of these technologies. This workshop is a hub for advancing the future of spatially-aware AI, from core reasoning to real-world application and responsible deployment.
Workshop
Abstract
This workshop aims to advance the state-of-the-art in long multi-scene video modelling, covering generation, understanding, evaluation, and ethical considerations. Long videos offer a powerful means of expression and communication, with applications in diverse fields such as entertainment, education, and health. However, current video generation and understanding techniques are typically confined to short, single-scene videos, limiting both our ability to create and comprehend complex video narratives. Thus, a growing need and research area is the development of methods for generating and understanding long-form videos of multiple dynamic scenes.
Workshop
Abstract
The VOTS2025 workshop is the thirteenth annual benchmarking activity of the VOT initiative, which has successfully identified key trends in tracking research, most recently the rise of video segmentation models as a promising direction for general object tracking. Continuing to connect the tracking community, VOTS2025 pushes the boundaries of tracking research. The workshop will present results of 32 trackers from three sub-challenges, focusing on holistic targets, targets undergoing topological transformations, and real-time tracking. Additionally, the program features presentations of winning methods, a panel discussion, and keynotes outlining future directions in object tracking and video understanding.
Workshop
Abstract
The workshop focuses on the topic of scene graphs and graph representation learning for visual perception applications in different domains. Through a series of keynote talks, the audience will learn about defining, generating and predicting scene graphs, as well as about employing them for other tasks. Oral presentations of accepted submissions to the workshop will further enrich discussed topics with state-of-the-art advancements and engage the community. The objective is for attendees to learn about current developments and application domains of scene graphs and graph representation learning, as well as to draw inspiration and identify commonalities across these domains. Furthermore, this workshop will create an opportunity to discuss limitations, challenges, and next steps from research, practical, and ethical perspectives.
Workshop
Abstract
The ninth edition of this workshop will emphasize the use of multimodal LLMs for hand-related tasks. Multimodal LLMs have revolutionized the perceptions of AI, and demonstrated groundbreaking contributions to multimodal understanding, zero-shot learning, and transfer learning. Those models can process and integrate information from different types of hand data (or modalities), allowing the model to better understand complex hand-object/-hand interaction situations by capturing richer, more diverse representations.
Workshop
Abstract
The objective of this workshop is to bring together engineers and researchers from academia and industry to discuss the current state-of-the-art methods in the field and challenges of computer vision for 3D scene generation, 3D scene reconstruction, 3D compositional scene geometric representation learning at a large scale. Moreover, this edition of workshop will also highlight 3D scene generation and understanding from multimodal data such as video, audio, and text, driven by its growing range of industrial applications.
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