Self-Critical Attention Learning for Person Re-Identification

In this paper, we propose a self-critical attention learning method for person re-identification. Unlike most existing methods which train the attention mechanism in a weakly-supervised manner and ignore the attention confidence level, we learn the attention with a critic which measures the attention quality and provides a powerful supervisory signal to guide the learning process. Moreover, the critic model facilitates the interpretation of the effectiveness of the attention mechanism during the learning process, by estimating the quality of the attention maps. Specifically, we jointly train our attention agent and critic in a reinforcement learning manner, where the agent produces the visual attention while the critic analyzes the gain from the attention and guides the agent to maximize this gain. We design spatial- and channel-wise attention models with our critic module and evaluate them on three popular benchmarks including Market-1501, DukeMTMC-ReID, and CUHK03. The experimental results demonstrate the superiority of our method, which outperforms the state-of-the-art methods by a large margin of 5.9%/2.1%, 6.3%/3.0%, and 10.5%/9.5% on mAP/Rank-1, respectively.