Implicit Alignment with Complementary Information for Text-based Person Re-identification

Deep-Person: Learning discriminative deep features for person Re-Identification

Domain generalizable (DG) person re-identification (ReID) aims to test across unseen domains without access to the target domain data at training time, which is a realistic but challenging problem. In contrast to methods assuming an identical model for different domains, Mixture of Experts (MoE) exploits multiple domain-specific networks for leveraging complementary information between domains, obtaining impressive results. However, prior MoE-based DG ReID methods suffer from a large model size with the increase of the number of source domains, and most of them overlook the exploitation of domain-invariant characteristics. To handle the two issues above, this paper presents a new approach called Mimic Embedding via adapTive Aggregation (\(\textsf{META}\)) for DG person ReID. To avoid the large model size, experts in \(\textsf{META}\) do not adopt a branch network for each source domain but share all the parameters except for the batch normalization layers. Besides multiple experts, \(\textsf{META}\) leverages Instance Normalization (IN) and introduces it into a global branch to pursue invariant features across domains. Meanwhile, \(\textsf{META}\) considers the relevance of an unseen target sample and source domains via normalization statistics and develops an aggregation module to adaptively integrate multiple experts for mimicking unseen target domain. Benefiting from a proposed consistency loss and an episodic training algorithm, \(\textsf{META}\) is expected to mimic embedding for a truly unseen target domain. Extensive experiments verify that \(\textsf{META}\) surpasses state-of-the-art DG person ReID methods by a large margin. Our code is available at https://github.com/xbq1994/META. KeywordsDomain generalizationPerson re-identification

Active learning has recently attracted increasing attention in the task of person re-identification, due to its unique scalability that not only maximally reduces the annotation cost but also retains the satisfying performance. Although some preliminary active learning methods have been explored in scalable person re-identification task, they have the following two problems: 1) the inefficiency in the selection process of image pairs due to the huge search space, and 2) the ineffectiveness caused by ignoring the impact of unlabeled data in model training. Considering that, we propose a Multi-grained Active Semi-Supervised learning framework, named MASS, to address the scalable person re-identification problem existing in the practical scenarios. Specifically, we firstly design a cluster-scatter procedure to alleviate the inefficiency problem, which consists of two components: cluster step and scatter step. The cluster step shrinks the search space into individual small clusters by a coarse-grained clustering method, and the subsequent scatter step further mines the hard distinguished image pairs from unlabelled set to purify the learned clusters by a novel centrality-based adaptive purification strategy. Afterward, we introduce a customized purification loss for the purified clustering, which utilizes the complementary information in both labeled and unlabeled data to optimize the model for solving the ineffectiveness problem. The cluster-scatter procedure and the model optimization are performed in an iterative fashion to achieve the promising performance while greatly reducing the annotation cost. Extensive experimental results have demonstrated that MASS can even achieve a competitive performance with fully supervised methods in the case of extremely less annotation requirements.

Visible-infrared person re-identification (VI-ReID) aims to match pedestrian images captured under visible and infrared modalities, which suffer from significant domain discrepancies. Existing approaches either synthesize cross-modal images or learn modality-invariant representations, yet often encounter semantic degradation or limited alignment capacity. Recent vision-language models leverage textual semantics for modality bridging; however, CLIP-based frameworks typically rely on learnable token proxies with limited expressiveness. In this paper, we propose a novel semantic-driven framework that explicitly generates rich, modality-agnostic textual descriptions from images as alignment cues. Specifically, we design a dual-branch Text Semantic Generation Module that includes: (1) an Attribute-Aware text descriptions Generation module using prompt-based templates to capture modality-invariant identity cues, and (2) a Contour-Aware text prompt Module that provides complementary structural information often missing in textual descriptions. To reconcile semantic heterogeneity, a Text Re-definition Module(TRM) fuses instance-level and class-level semantics into unified representations, enabling fine-grained alignment with image features. Furthermore, we construct category-level textual prototypes as global semantic anchors to enhance cross-modal consistency. Extensive experiments on two standard VI-ReID benchmarks demonstrate that our method achieves superior performance, validating its effectiveness in semantic-guided modality alignment.

Multi-modal person Re-ID introduces more complementary information to assist the traditional Re-ID task. Existing multi-modal methods ignore the importance of modality-specific information in the feature fusion stage. To this end, we propose a novel method to boost modality-specific representations for multi-modal person Re-ID: Interact, Embed, and EnlargE (IEEE). First, we propose a cross-modal interacting module to exchange useful information between different modalities in the feature extraction phase. Second, we propose a relation-based embedding module to enhance the richness of feature descriptors by embedding the global feature into the fine-grained local information. Finally, we propose multi-modal margin loss to force the network to learn modality-specific information for each modality by enlarging the intra-class discrepancy. Superior performance on multi-modal Re-ID dataset RGBNT201 and three constructed Re-ID datasets validate the effectiveness of the proposed method compared with the state-of-the-art approaches.