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.