Composed Image Retrieval

Composed image retrieval studies how to find a target image from a query that combines a reference image with an instruction, such as changing an outfit while preserving its style. Methods learn joint image–text embeddings from paired examples, or adapt pretrained vision-language models with projection, interpolation, or instruction-based composition; reranking and interactive clarification can further refine results. Work spans fashion and product search as well as medical and other domain-specific retrieval, with both supervised training and zero-shot approaches that use pretrained models without task-specific training. A central challenge is balancing broad semantic understanding with fine-grained attribute and instance fidelity, while ensuring benchmarks test genuine multimodal composition rather than shortcuts and remain robust to ambiguity and noisy labels.