发表机构
Beijing Jiaotong University（北京交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对零样本组合图像检索中查询表示的不匹配问题，提出将任务重构为目标状态重建后检索，并设计了无需训练的ASAP-CIR框架，利用冻结多模态大语言模型重建静态目标表示，实验表明该原理在多个基准上有效，尤其在多目标CIRCO上提升显著。
AI 中文摘要
组合图像检索（CIR）旨在根据由参考图像和修改文本组成的查询来检索所需的目标图像。该任务表现出一种不寻常的表示不对称性：修改文本指定了从参考状态的转换，而检索候选项描绘的是完成的目标状态。这为直接使用面向转换的语言查询预训练视觉-语言空间的零样本方法造成了表示不匹配。我们研究了这种不匹配，并将零样本组合图像检索重新表述为目标状态重建后检索。我们通过ASAP-CIR实例化了这一表述，这是一个无需训练的框架，使用冻结的多模态大语言模型（MLLM）重建静态目标表示。该表示结合了多个整体描述与一组具有重要性权重的原子语义，从而保留了整体目标身份和细粒度视觉约束。检索随后整合了整体状态对齐、原子约束锚定和校准的目标状态证据聚合。一项受控的纯文本诊断表明，目标侧静态查询表述比动态组合查询表述能实现更可靠的检索，尤其是在需要抑制或转换源状态语义时。在FashionIQ、CIRR和CIRCO上的实验进一步表征了该表示原理的有效性和局限性，其中在多目标CIRCO基准上取得了最明显的改进。这些结果表明，在检索之前如何表示组合意图是一个重要的设计选择，与检索骨干网络本身的选择不同。
英文摘要
Composed image retrieval (CIR) aims to retrieve a desired target image from a query consisting of a reference image and a modification text. This task exhibits an unusual representational asymmetry: the modification text specifies a transition from the reference state, whereas retrieval candidates depict completed target states. This creates a representation mismatch for zero-shot methods that query pretrained vision-language spaces directly with transformation-oriented language. We study this mismatch and reformulate zero-shot composed image retrieval as target-state reconstruction followed by retrieval. We instantiate this formulation with ASAP-CIR, a training-free framework that reconstructs a static target representation using a frozen multimodal large language model (MLLM). The representation combines multiple holistic descriptions with a variable set of importance-weighted atomic semantics, thereby preserving both overall target identity and fine-grained visual constraints. Retrieval then integrates holistic state alignment, atomic constraint grounding, and calibrated target-state evidence aggregation. A controlled text-only diagnostic shows that target-side static query formulations achieve more reliable retrieval than dynamic composed query formulations, particularly when source-state semantics must be suppressed or transformed. Experiments on FashionIQ, CIRR, and CIRCO further characterize the effectiveness and limitations of this representation principle, with the clearest gains on the multi-target CIRCO benchmark. These results show that how composed intent is represented before retrieval is a consequential design choice, distinct from the choice of retrieval backbone itself.
Comments24 pages, 8 figures, including appendices
