Typical person re-identification (ReID) methods usually describe each pedestrian with a single feature vector and match them in a task-specific metric space. However, the methods based on a single feature vector are not sufficient enough to overcome visual ambiguity, which frequently occurs in real scenario. In this paper, we propose a novel end-to-end trainable framework, called Dual ATtention Matching network (DuATM), to learn context-aware feature sequences and perform attentive sequence comparison simultaneously. The core component of our DuATM framework is a dual attention mechanism, in which both intra-sequence and inter-sequence attention strategies are used for feature refinement and feature-pair alignment, respectively. Thus, detailed visual cues contained in the intermediate feature sequences can be automatically exploited and properly compared. We train the proposed DuATM network as a siamese network via a triplet loss assisted with a de-correlation loss and a cross-entropy loss. We conduct extensive experiments on both image and video based ReID benchmark datasets. Experimental results demonstrate the significant advantages of our approach compared to the state-of-the-art methods.

本文提出了一种双重注意力匹配网络（DuATM）框架，其核心组件是双重注意力机制，通过多种策略，自动利用并比较中间特征序列中包含的详细视觉提示，实现了一种上下文感知的特征提取和比较方法，通过三元损失和去相关损失和交叉熵损失等多种方法进行了训练，结果表明该方法在图像和视频ReID基准数据集上都优于现有最先进的方法。

基于上下文感知特征序列的双关注匹配网络用于人物再识别