Deep attention aware feature learning for person re-Identification. (June 2022) - Chen, Yifan; Wang, Han; Sun, Xiaolu; Fan, Bin; Tang, Chu; Zeng, Hui
Highlights: We propose to learn global and local attention aware features for person ReID. Two additional branches are introduced to realize the proposed attention aware feature learning in the training stage, and they are removed in the inference time to keep the same model size and inference speed. Ablation studies and visualization results are included to help understanding the proposed method. Significant performance improvements over existing methods are achieved on five widely used benchmarks. Abstract: Visual attention has proven to be effective in improving the performance of person re-identification. Most existing methods apply visual attention heuristically by learning an additional attention map to re-weight the feature maps for person re-identification, however, this kind of methods inevitably increase the model complexity and inference time. In this paper, we propose to incorporate the ability of predicting attention maps as additional objectives in a person ReID network without changing the original structure, thus maintain the same inference time and model size. Two kinds of attention maps have been considered to make the learned feature maps being aware of the person and related body parts respectively. Globally, a holistic attention branch (HAB) is proposed to make the feature maps obtained by backbone could focus on persons so as to alleviate the influence of background. Locally, a partial attention branch (PAB) is proposed to make the extracted features can be decoupled into several groups that are separately responsible for different body parts, thus increasing the robustness to pose variation and partial occlusion. These two kinds of attentions are universal and can be incorporated into existing ReID networks. We have tested its performance on two typical networks (TriNet [1] and Bag of Tricks [2]) and observed significant performance improvement on five widely used datasets.