Despite the remarkable progress in recent years, person Re-Identification (ReID) approaches frequently fail in cases where the semantic body parts are misaligned between the detected human boxes. To mitigate such cases, we propose a novel High-Order ReID (HOReID) framework that enables semantic pose alignment by aggregating the fine-grained part details of multilevel feature maps. The HOReID adopts a high-order mapping of multilevel feature similarities in order to emphasize the differences of the similarities between aligned and misaligned part pairs in two person images. Since the similarities of misaligned part pairs are reduced, the HOReID enhances pose-robustness within the learned features. We show that our method derives from an intuitive and interpretable motivation and elegantly reduces the misalignment problem without using any prior knowledge from human pose annotations or pose estimation networks. This paper theoretically and experimentally demonstrates the effectiveness of the proposed HOReID, achieving superior performance over the state-of-the-art methods on the four large-scale 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.

With the rapid increase of large-scale and real-world person datasets, it is crucial to address the problem of long-tailed data distributions, i.e., head classes have large number of images while tail classes occupy extremely few samples. We observe that the imbalanced data distribution is likely to distort the overall feature space and impair the generalization capability of trained models. Nevertheless, this long-tailed problem has been rarely investigated in previous person Re-Identification (ReID) works. In this paper, we propose a novel Long-Tailed Re-Identification (LTReID) framework to simultaneously alleviate class-imbalance and hard-imbalance problems. Specifically, each real feature is decomposed into multiple independent components with two decorrelation losses. Then these components are randomly aggre- gated to generate more fake features for tail classes than head ones, resulting in the class-balance between head and tail classes. For the hard-balance between easy and hard samples, we utilize adversarial learning to generate more hard features than easy ones. The proposed framework can be trained in an end-to-end manner and avoids increasing the space and time complexity of inference models. Moreover, comprehensive experiments are conducted on the four ReID datasets so as to validate the effectiveness of the overall framework and the advantage of each module. Our results show that when trained with either balanced or imbalanced datasets, the LTReID achieves superior performance over the state-of-the-art methods.

As person image variations are likely to cause a part misalignment problem, most previous person Re-Identification (ReID) works may adopt local feature partition or additional landmark annotations to acquire aligned person features and boost ReID performance. However, such approaches either only achieve coarse-grained part alignments without considering detailed image variations within each part, or require extra annotated landmarks to train an available pose estimation model. In this work, we propose an effective Discriminable and Reliable Transformer (DRFormer) framework to learn part-aligned person representations with only person identity labels. Specifically, the DRFormer framework consists of Discriminable Feature Transformer (DFT) and Reliable Feature Transformer (RFT) modules, which generate discriminable and reliable high-order features, respectively. For reducing the dimension of high-order features, the DFT module utilizes a Self-Attentive Kronecker Product (SAKP) algorithm to promote the representational capabilities of compressed features via a self-attention strategy. For eliminating the background noise, the RFT module mines the foreground regions to adaptively aggregate foreground features via a Gumbel-Softmax strategy. Moreover, the proposed framework derives from an interpretable motivation and elegantly solves part misalignments without using feature partition or pose estimation. This paper theoretically and experimentally demonstrates the superiority of the proposed DRFormer framework, achieving state-of-the-art performance on various person ReID datasets.