# Composed Image Retrieval

Composed image retrieval takes a reference image plus a modification text and must return the target image, and the common recipe compresses that multimodal query straight into one retrieval vector, leaving the model no explicit chance to work out what the text changes. FactEmbed inserts a set of learnable factorized latents into Qwen2.5-VL-7B's input and trains them with a target-imagination objective that reconstructs target details, so each latent grounds a semantic region, then carries that decomposition into retrieval through a factor-level contrastive loss and attention distillation. On the refined CIRR and FashionIQ benchmarks it scores above both CSMCIR and ReCALL on Recall@1 and average Recall@10, though the improvement margins stated in the text do not match the table values.

Composed image retrieval takes a reference image plus a modification text and must return the target image, and the common recipe compresses that multimodal query straight into one retrieval vector, leaving the model no explicit chance to work out what the text changes. FactEmbed inserts a set of learnable factorized latents into Qwen2.5-VL-7B's input and trains them with a target-imagination objective that reconstructs target details, so each latent grounds a semantic region, then carries that decomposition into retrieval through a factor-level contrastive loss and attention distillation. On the refined CIRR and FashionIQ benchmarks it scores above both CSMCIR and ReCALL on Recall@1 and average Recall@10, though the improvement margins stated in the text do not match the table values.