A CAM-Guided Parameter-Free Attention Network for Person Re-Identification

Most existing attention mechanisms have no supervised signal during the training phase, which limits the model feature learning capability. To solve this problem, we propose a novel parameter-free attention mechanism based on class activation mapping. Attention mechanisms usually consist of spatial attention and channel attention, which indicates that 'where' and 'what' is more meaningful, respectively. Our attention also contains both types of attention. For Spatial Attention, we use class activation mapping as a supervision signal to guide the generation of it directly in space. Thus our spatial attention can pay more attention to the informative pedestrian parts of the scene and reduce background interference. For Channel Attention, the importance of each channel is obtained by the similarity between the aforementioned spatial attention and the feature map of each channel. In this manner, our channel attention is indirectly guided by class activation mapping. In addition, our attention is parameter-free, which reduces the risk of over-fitting. Finally, we conduct extensive evaluations on three popular benchmark datasets including Market1501, DukeMTMC-reID, and MSMT17, demonstrating the effectiveness of our approach on discriminative person representations.