Context Sensing Attention Network for Video-based Person Re-identification

Citations Over TimeTop 20% of 2022 papers

Abstract

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.

Related Papers

• Automobile Form Feature and Feature Line(2007)
• → Avoidance of Feature Interactions at Run-Time(2006)3 cited
• → Avoidance of Feature Interactions at Run-Time(2006)2 cited
• Application Study on Feature Modeling for Box Part(2008)
• Observed feature PlZPZ correspondkto model feature BOTTOM-SIDE In the model Observed feature P5-P6 corresponds to model feature TOP-FACE In the model Observed feature P4-P5 couesponds to model feature TOP-SIDE Observed feature P3-Pfi* corresponds to model feature SIDE-FACE-B In the model In the model(1987)