面向泛化型RGB-红外行人重识别的记忆调节与对齐
Memory Regulation and Alignment toward Generalizer RGB-Infrared Person
AI总结：
针对RGB-红外行人重识别中的领域偏移问题，提出多粒度记忆调节与对齐模块（MG-MRA），通过引入潜在变量属性缓解模型过度自信，并利用稀疏属性度量图像相似度，在RegDB和SYSU-MM01上取得SOTA表现。
摘要：
源于不可忽视的模态差异以及训练集和测试集之间不重叠的身份类别，领域偏移是RGB-红外行人重识别的一个主要问题。解决领域偏移这一固有问题的关键在于使两个领域的数据分布相似。然而，RGB-IR ReID总是需要判别性特征，导致对可见类别的特征敏感性过度依赖，例如通过基于注意力的特征对齐或度量学习。因此，从预定义的训练类别中预测未见查询类别可能并不准确，并会导致次优的对抗梯度。本文以更具解释性的方式揭示了这一点，并提出了一种新颖的多粒度记忆调节与对齐模块（MG-MRA）来解决该问题。通过将潜在变量属性（从细粒度到粗语义粒度）显式融入中间特征，我们的方法能够缓解模型对可见类别判别性特征的过度自信。此外，不再通过遍历最近邻来匹配判别性特征，而是根据特征重新收集稀疏属性（即全局结构模式），并将其分配用于在哈希中度量成对图像相似度。在RegDB和SYSU-MM01上的大量实验表明，所提方法优于现有的最先进方法。代码可在https://github.com/Chenfeng1271/MGMRA获取。
英文摘要：
The domain shift, coming from unneglectable modality gap and non-overlapped identity classes between training and test sets, is a major issue of RGB-Infrared person re-identification. A key to tackle the inherent issue -- domain shift -- is to enforce the data distributions of the two domains to be similar. However, RGB-IR ReID always demands discriminative features, leading to over-rely feature sensitivity of seen classes, \textit{e.g.}, via attention-based feature alignment or metric learning. Therefore, predicting the unseen query category from predefined training classes may not be accurate and leads to a sub-optimal adversarial gradient. In this paper, we uncover it in a more explainable way and propose a novel multi-granularity memory regulation and alignment module (MG-MRA) to solve this issue. By explicitly incorporating a latent variable attribute, from fine-grained to coarse semantic granularity, into intermediate features, our method could alleviate the over-confidence of the model about discriminative features of seen classes. Moreover, instead of matching discriminative features by traversing nearest neighbor, sparse attributes, \textit{i.e.}, global structural pattern, are recollected with respect to features and assigned to measure pair-wise image similarity in hashing. Extensive experiments on RegDB \cite{RegDB} and SYSU-MM01 \cite{SYSU} show the superiority of the proposed method that outperforms existing state-of-the-art methods. Our code is available in https://github.com/Chenfeng1271/MGMRA.
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。
