Group Re-Identification with Hybrid Attention Model and Residual Distance

Group re-identification (Re-ID) is an important task of computer vision and involves multiple challenges. In this paper, we propose a Hybrid Attention Model (HAM) to address the problem of group Re-ID, solving the spatial variation in the challenging still-image-based group Re-ID task. HAM consists of both the position and channel attention to make the network focus more on the crucial areas and features of the group images. Furthermore, we propose a novel Least Squares Residual Distance (LSRD) based on the least squares algorithm. LSRD can leverage the residual of the fitting function achieved by least squares method, better learning the metric between group image pairs. To evaluate the performance, we propose a new largest group Re-ID dataset. Extensive experimental results conducted on the dataset demonstrate the effectiveness of our approaches.