ConeSep: 基于锥的鲁棒噪声-反学习组合网络用于组合图像检索
ConeSep: Cone-based Robust Noise-Unlearning Compositional Network for Composed Image Retrieval
AI总结：
本文针对组合图像检索中噪声三元组对应问题，提出ConeSep网络，解决模态抑制、负锚缺乏和反学习反作用三大挑战，通过几何保真量化、负边界学习和边界基于的目标反学习方法，提升检索性能。
摘要：
组合图像检索（CIR）任务通过参考图像和修改文本提供灵活的检索范式，但严重依赖昂贵且易错的三元组标注。本文系统研究了由标注引入的噪声三元组对应（NTC）问题。我们发现，NTC噪声，特别是"hard noise"（即参考和目标图像高度相似但修改文本错误），对现有噪声对应学习（NCL）方法构成独特挑战，因为它破坏了传统的"小损失假设"。我们识别并阐明了NTC任务中的三个关键但被忽视的挑战，即（C1）模态抑制，（C2）负锚缺乏，以及（C3）反学习反作用。为了解决这些挑战，我们提出了一种基于锥的鲁棒噪声-反学习组合网络（ConeSep）。具体而言，我们首先提出几何保真量化，理论上建立并实际估计噪声边界，以精确定位噪声对应。接着，我们引入负边界学习，为每个查询学习其显式语义相反锚在嵌入空间中。最后，我们设计基于边界的针对性反学习，将噪声校正过程建模为最优传输问题，优雅地避免反学习反作用。在基准数据集（FashionIQ和CIRR）上的广泛实验表明，ConeSep显著优于当前最先进的方法，充分证明了我们方法的有效性和鲁棒性。
英文摘要：
The Composed Image Retrieval (CIR) task provides a flexible retrieval paradigm via a reference image and modification text, but it heavily relies on expensive and error-prone triplet annotations. This paper systematically investigates the Noisy Triplet Correspondence (NTC) problem introduced by annotations. We find that NTC noise, particularly ``hard noise'' (i.e., the reference and target images are highly similar but the modification text is incorrect), poses a unique challenge to existing Noise Correspondence Learning (NCL) methods because it breaks the traditional ``small loss hypothesis''. We identify and elucidate three key, yet overlooked, challenges in the NTC task, namely (C1) Modality Suppression, (C2) Negative Anchor Deficiency, and (C3) Unlearning Backlash. To address these challenges, we propose a Cone-based robuSt noisE-unlearning comPositional network (ConeSep). Specifically, we first propose Geometric Fidelity Quantization, theoretically establishing and practically estimating a noise boundary to precisely locate noisy correspondence. Next, we introduce Negative Boundary Learning, which learns a ``diagonal negative combination'' for each query as its explicit semantic opposite-anchor in the embedding space. Finally, we design Boundary-based Targeted Unlearning, which models the noisy correction process as an optimal transport problem, elegantly avoiding Unlearning Backlash. Extensive experiments on benchmark datasets (FashionIQ and CIRR) demonstrate that ConeSep significantly outperforms current state-of-the-art methods, which fully demonstrates the effectiveness and robustness of our method.
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。
