DQE-CIR: 通过可学习的属性权重和目标相对负采样实现区分性查询嵌入
DQE-CIR: Distinctive Query Embeddings through Learnable Attribute Weights and Target Relative Negative Sampling in Composed Image Retrieval
AI总结：
DQE-CIR通过可学习属性权重和目标相对负采样提升复合图像检索中查询嵌入的区分性，解决语义混淆和相关图像被推远的问题。
摘要：
复合图像检索（CIR）旨在通过联合解释参考图像和指定预期变化的修改文本来检索目标图像。大多数现有方法仍然基于对比学习框架，将真实图像视为唯一的正例，其余图像均视为负例。这种策略不可避免地引入了相关但有效的图像被错误推远的现象，以及不同修改意图在嵌入空间中重叠的语义混淆。因此，学习到的查询表示通常缺乏判别性，特别是在细粒度属性修改方面。为克服这些限制，我们提出了通过可学习的属性权重和目标相对负采样（DQE-CIR）实现区分性查询嵌入的方法，该方法通过在训练过程中显式建模目标相对相关性来学习区分性查询嵌入。DQE-CIR结合了可学习的属性加权，以强调受修改文本条件下的独特视觉特征，从而在语言和视觉之间实现更精确的特征对齐。此外，我们引入了目标相对负采样，该方法构建了目标相对相似性分布，并从排除简单负例和模糊假负例的中区区域中选择信息丰富的负例。这种策略通过提高查询判别性和减少语义相似但无关候选人的混淆，实现了对细粒度属性变化的更可靠检索。
英文摘要：
Composed image retrieval (CIR) addresses the task of retrieving a target image by jointly interpreting a reference image and a modification text that specifies the intended change. Most existing methods are still built upon contrastive learning frameworks that treat the ground truth image as the only positive instance and all remaining images as negatives. This strategy inevitably introduces relevance suppression, where semantically related yet valid images are incorrectly pushed away, and semantic confusion, where different modification intents collapse into overlapping regions of the embedding space. As a result, the learned query representations often lack discriminativeness, particularly at fine-grained attribute modifications. To overcome these limitations, we propose distinctive query embeddings through learnable attribute weights and target relative negative sampling (DQE-CIR), a method designed to learn distinctive query embeddings by explicitly modeling target relative relevance during training. DQE-CIR incorporates learnable attribute weighting to emphasize distinctive visual features conditioned on the modification text, enabling more precise feature alignment between language and vision. Furthermore, we introduce target relative negative sampling, which constructs a target relative similarity distribution and selects informative negatives from a mid-zone region that excludes both easy negatives and ambiguous false negatives. This strategy enables more reliable retrieval for fine-grained attribute changes by improving query discriminativeness and reducing confusion caused by semantically similar but irrelevant candidates.
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。
