Factorized MLLM Embeddings for Composed Image Retrieval
FactEmbed is a method for composed image retrieval, which takes a reference image plus a modification text to find a target image.
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- Primary sourceHugging Face
- Discovery linkHugging Face
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FactEmbed is a method for composed image retrieval, which takes a reference image plus a modification text to find a target image. It uses factorized latents trained with an intermediate target imagination objective to simulate intended visual modifications before forming retrieval embeddings. A factor-level contrastive loss is optimized, and decomposed representations are distilled into a deeper attention layer of the MLLM. At inference, a global [EOS]-based embedding is used, and mean-pooled factorized latents rerank a short candidate list. The authors report that FactEmbed outperforms prior works across CIR benchmarks.
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FactEmbed uses factorized latents that simulate intended visual modifications through an intermediate target imagination objective.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseA factor-level contrastive loss is optimized while distilling decomposed representations into a deeper MLLM attention layer.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseMean-pooled factorized latents rerank a short candidate list at negligible cost, produced by the same forward pass as the [EOS] embedding.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseLimits from the stored brief
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Why this may matter
Composed image retrieval combines image and text cues that are hard to express alone. Explicitly resolving modifications before embedding may make MLLMs' compositional understanding more useful for retrieval systems.
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