Yanyun Zhao

As person parts are frequently misaligned between detected human boxes, an image representation that can handle this part misalignment is required. In this work, we propose an effective grouped attentive re-identification (GAReID) framework to learn part-aligned and background robust representations for person re-identification (ReID). Specifically, the GAReID framework consists of grouped high-order pooling (GHOP) and attentive high-order pooling (AHOP) layers, which generate high-order image and foreground features, respectively. In addition, a novel grouped Kronecker product (GKP) is proposed to use both channel group and shuffle strategies for high-order feature compression, while promoting the representational capabilities of compressed high-order features. We show that our method derives from an interpretable motivation and elegantly reduces part misalignments without using landmark detection or feature partition. This article theoretically and experimentally demonstrates the superiority of the GAReID framework, achieving state-of-the-art performance on various person ReID datasets.

\textit{Visible Thermal Person Re-Identification} (VTReID) is a cross-modality retrieval problem in computer vision. Accurate VTReID is very challenging due to large modality discrepancies. In this work, we design a novel \textit{Multi-Patch Matching Network} (MPMN) framework to simultaneously mitigate the heterogeneity of coarse-grained and fine-grained visual semantics. In view of cross-modality matching, we verify that aligning modality distributions of the original features is likely to suffer from the selective alignment behavior, \ie, only focuses on easiest dimensions or subspaces. Inspired by adversarial learning, we propose a new \textit{Multi-Patch Modality Alignment} (MPMA) loss to jointly balance and reduce the modality discrepancies of multi-patch features by mining hard subspaces and abandoning easy subspaces. Since multi-patch features are potentially complementary to each other, the semantic correlations between different patches should be exploited during training. Motivated by knowledge distillation, we put forward a new \textit{Cross-Patch Correlation Distillation} (CPCD) loss to transfer the semantic knowledges across different patches. To balance multi-patch tasks, an effective \textit{Patch-Aware Priority Attention} (PAPA) method is further introduced to dynamically prioritize hard patch tasks during training. This paper experimentally demonstrates the effectiveness of the proposed methods, achieving superior performance over the state-of-the-art methods on RegDB and SYSU-MM01 datasets.