  Video-based person re-identification (ReID) is challenging due to the
presence of various interferences in video frames. Recent approaches handle
this problem using temporal aggregation strategies. In this work, we propose a
novel Context Sensing Attention Network (CSA-Net), which improves both the
frame feature extraction and temporal aggregation steps. First, we introduce
the Context Sensing Channel Attention (CSCA) module, which emphasizes responses
from informative channels for each frame. These informative channels are
identified with reference not only to each individual frame, but also to the
content of the entire sequence. Therefore, CSCA explores both the individuality
of each frame and the global context of the sequence. Second, we propose the
Contrastive Feature Aggregation (CFA) module, which predicts frame weights for
temporal aggregation. Here, the weight for each frame is determined in a
contrastive manner: i.e., not only by the quality of each individual frame, but
also by the average quality of the other frames in a sequence. Therefore, it
effectively promotes the contribution of relatively good frames. Extensive
experimental results on four datasets show that CSA-Net consistently achieves
state-of-the-art performance.
