Multi-Granularity Graph-Convolution-Based Method for Weakly Supervised Person Search

One-step Weakly Supervised Person Search (WSPS) jointly performs pedestrian detection and person Re-IDentification (ReID) only with bounding box annotations, which makes the traditional person ReID problem more practical and efficient for real-world applications. However, this task is very challenging due to the following reasons: 1) large feature gap between person ReID and general object detection tasks when learning shared representations; 2) difficult pseudo identity estimation for each person image with unrefined raw detection and dramatic scale changes. To address the above issues, we propose a multi-granularity graph convolution framework to jointly optimize the aligned task features, as well as to assist the pseudo label estimation. Specifically, the multi-granularity feature alignment module (MFA) in the designed two-branch framework employs cluster-level bi-directional interaction of various granularity information to narrow down the large feature gap. Further, upon the MFA module, we introduce the multi-granularity neighbor-guided graph-convolution-based pseudo-label estimation module, to enhance feature representations for distinguishing diverse identities. Extensive experimental results demonstrate the effectiveness of the proposed method, and show superior performances to state-of-the-art methods by a large margin on the CUHK-SYSU and PRW datasets.