Image Retrieval
Overview
Image retrieval studies how to find relevant images in large collections using images, text, sketches, metadata, or combinations of these. Common approaches learn shared vision-language embeddings with contrastive training, then improve fine-grained matching with regions or patches, reranking, and compact hashes or indexes. Composed and conversational retrieval adds user-described changes or constraints, while applications range from products and people to medical images, geolocation, and scientific data. A central challenge is balancing broad, efficient retrieval with precise semantic and local matching, while benchmarks must distinguish genuine multimodal understanding from shortcuts or noisy relevance labels.
Latest papers
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