增强推理的表示用于多模态检索
Reasoning-Augmented Representations for Multimodal Retrieval
AI总结：
本研究提出一种增强推理的多模态检索方法，通过外部化推理和语义密集表示提升检索性能，尤其在知识密集型查询和组合修改请求中表现优异。
摘要：
通用多模态检索（UMR）旨在实现文本和视觉之间的任意到任意搜索，但现代嵌入模型在查询需要潜在推理（例如解决不明确的参考或匹配组合约束）时仍然容易出错。我们认为这种脆弱性往往是数据引起的：当图像携带
英文摘要：
Universal Multimodal Retrieval (UMR) seeks any-to-any search across text and vision, yet modern embedding models remain brittle when queries require latent reasoning (e.g., resolving underspecified references or matching compositional constraints). We argue this brittleness is often data-induced: when images carry "silent" evidence and queries leave key semantics implicit, a single embedding pass must both reason and compress, encouraging spurious feature matching. We propose a data-centric framework that decouples these roles by externalizing reasoning before retrieval. Using a strong Vision--Language Model, we make implicit semantics explicit by densely captioning visual evidence in corpus entries, resolving ambiguous multimodal references in queries, and rewriting verbose instructions into concise retrieval constraints. Inference-time enhancement alone is insufficient; the retriever must be trained on these semantically dense representations to avoid distribution shift and fully exploit the added signal. Across M-BEIR, our reasoning-augmented training method yields consistent gains over strong baselines, with ablations showing that corpus enhancement chiefly benefits knowledge-intensive queries while query enhancement is critical for compositional modification requests. We publicly release our code at https://github.com/AugmentedRetrieval/ReasoningAugmentedRetrieval.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
