CVPR 2023 - Shape-Erased Feature Learning for Visible-Infrared Person Re-Identification

In this episode we discuss Shape-Erased Feature Learning for Visible-Infrared Person Re-Identification by Jiawei Feng, Ancong Wu, Wei-Shi Zheng. The paper proposes a new approach to address the challenging problem of visible-infrared person re-identification (VI-ReID) by learning diverse modality-shared semantic concepts. The proposed method aims to force the ReID model to extract more and different modality-shared features for identification by erasing body-shape-related semantic concepts in the learned features. This is achieved through a shape-erased feature learning paradigm that decorrelates modality-shared features in two orthogonal subspaces. The experimental results on three datasets demonstrate the effectiveness of the proposed method.