Composed Image Retrieval
Overview
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.
Latest papers
65 papers4 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.