Cascaded attention-guided multi-granularity feature learning for person re-identification

Attention mechanism has been extensively employed in the task of person re-identification, as it helps to extract much more discriminative feature representations. However, most of existing works either incorporate a single-scale attention module, or the embedded attentions work independently. Though promising results are achieved, they may fail to mine different subtle visual clues. To mitigate this issue, a novel framework called cascaded attention network (CANet) is proposed, which allows to mine diverse clues and integrate them into final multi-granularity features by a cascaded manner. Specifically, we design a novel hybrid pooling attention module (HPAM) and plug it into backbone network at different stages. To make them work collaboratively, an inter-attention regularization is applied, such that they can localize complementary salient features. Then, CANet extracts global and local features from a part-based pyramidal architecture. For better feature robustness, supervision is applied to not only the pyramidal branches, but also those intermediate attention modules. Furthermore, within each supervision branch, hybrid pooling with two different strides is executed to enhance feature representation capabilities. Extensive experiments with ablation analysis demonstrate the effectiveness of the proposed method, and state-of-the-art results are achieved on three public benchmark datasets, including Market-1501, CUHK03, and DukeMTMC-ReID.