Hierarchical Attentive Feature Aggregation for Person Re-Identification

Recent efforts on person re-identification have shown promising results by learning discriminative features via the multi-branch network. To further boost feature discrimination, attention mechanism has also been extensively employed. However, the branches on the main level rarely communicate with others in existing branching models, which may compromise the ability of mining diverse features. To mitigate this issue, a novel framework called Hierarchical Attentive Feature Aggregation (Hi-AFA) is proposed. In Hi-AFA, a hierarchical aggregation mechanism is applied to learn attentive features. The current feature map is not only fed into the next stage, but also aggregated into another branch, leading to hierarchical feature flows along depth and parallel branches. We also present a simple Feature Suppression Operation (FSO) and a Lightweight Dual Attention Module (LDAM) to guide feature learning. The FSO can partially erase the salient features already discovered, such that more potential clues can be mined by other branches with the help of LDAM. By this manner, the branches could cooperate to mine richer and more diverse feature representations. The hierarchical aggregation and multi-granularity feature learning are integrated into a unified architecture that builds upon OSNet, resulting a resource-economical and effective person re-identification model. Extensive experiments on four mainstream datasets, including Market-1501, DukeMTMC-reID, MSMT17, and CUHK03, are conducted to validate the effectiveness of the proposed method, and results show that state-of-the-art performance is achieved.