Representation Learning with Very Limited Resources: When Data; Modalities; Labels; and Computing Resources are Scarce

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.