Both fine-grained discriminative details and global semantic features can contribute to solving person re-identification challenges, such as occlusion and pose variations. Vision foundation models (\textit{e.g.}, DINO) excel at mining local textures, and vision-language models (\textit{e.g.}, CLIP) capture strong global semantic difference. Existing methods predominantly rely on a single paradigm, neglecting the potential benefits of their integration. In this paper, we analyze the complementary roles of these two architectures and propose a framework to synergize their strengths by a \textbf{D}ual-\textbf{R}egularized Bidirectional \textbf{Transformer} (\textbf{DRFormer}). The dual-regularization mechanism ensures diverse feature extraction and achieves a better balance in the contributions of the two models. Extensive experiments on five benchmarks show that our method effectively harmonizes local and global representations, achieving competitive performance against state-of-the-art methods.
Hierarchical Identity Learning for Unsupervised Visible-Infrared Person Re-Identification
IEEE Publication Technology Department（IEEE出版技术部门）；State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University（信息服务网络国家重点实验室，电信工程学院，西安电子科技大学）；Department of Computer Science and Technology, Tongji University（计算机科学与技术系，同济大学）
DINOv2 Driven Gait Representation Learning for Video-Based Visible-Infrared Person Re-identification
Kunming University of Science and Technology（昆明理工大学）；Chongqing University of Post and Telecommunications（重庆邮电大学）；China University of Mining Technology（中国矿业大学）；Nanjing University of Science and Technology（南京理工大学）
High performance person Re-Identification (Re-ID) requires the model to focus on both global silhouette and local details of pedestrian. To extract such more representative features, an effective way is to exploit deep models with multiple branches. However, most multi-branch based methods implemented by duplication of part backbone structure normally lead to severe increase of computational cost. In this paper, we propose a lightweight Feature Pyramid Branch (FPB) to extract features from different layers of networks and aggregate them in a bidirectional pyramid structure. Cooperated by atten…
Beijing University of Posts and Telecommunications（北京邮电大学）
Person reidentification (ReID) refers to the task of verifying the identity of a pedestrian observed from nonoverlapping views in a surveillance camera network. It has recently been validated that reranking can achieve remarkable performance improvements in person ReID systems. However, current reranking approaches either require feedback from users or suffer from burdensome computational costs. In this paper, we propose to exploit a density-adaptive smooth kernel technique to achieve efficient and effective reranking. Specifically, we adopt a smooth kernel function to formulate the neighbor r…
Multilingual Text-to-Image Person Retrieval via Bidirectional Relation Reasoning and Aligning
School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）；Key Laboratory of New Generation Artificial Intelligence Technology & Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（新 generation 人工智能技术及交叉应用重点实验室（东南大学），教育部，中国）
Learning Language-Driven Sequence-Level Modal-Invariant Representations for Video-Based Visible-Infrared Person Re-Identification
Shandong Key Laboratory of Ubiquitous Intelligent Computing, School of Information Science and Engineering, University of Jinan（山东省 Ubiquitous Intelligent Computing 重点实验室，济南大学信息科学与工程学院）；College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University（信息科学与技术及人工智能学院，南京林业大学）；Department of Informatics, Modeling, Electronics, and Systems, University of Calabria（信息学、建模、电子与系统系，卡利博大学）
QA-ReID: Quality-Aware Query-Adaptive Convolution Leveraging Fused Global and Structural Cues for Clothes-Changing ReID
School of Business, Stevens Institute of Technology（斯蒂文斯理工学院商学院）；Electrical and Computer Engineering, University of California, San Diego（加州大学圣地亚哥分校电子与计算机工程系）；Tandon School of Engineering, New York University（纽约大学塔恩工学院）；Institute of Operations Research and Analytics, National University of Singapore（新加坡国立大学运筹与分析研究所）
Bidirectional Progressive Transformer for Interaction Intention Anticipation
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
University of California Los Angeles（加州大学洛杉矶分校）；SAIC Innovation Center San Jose（上汽集团创新中心圣何塞分部）
Following the recent progress in image classification and captioning using deep learning, we develop a novel natural language person retrieval system based on an attention mechanism. More specifically, given the description of a person, the goal is to localize the person in an image. To this end, we first construct a benchmark dataset for natural language person retrieval. To do so, we generate bounding boxes for persons in a public image dataset from the segmentation masks, which are then annotated with descriptions and attributes using the Amazon Mechanical Turk. We then adopt a region propo…
Human pose estimation has been widely studied with much focus on supervised learning requiring sufficient annotations. However, in real applications, a pretrained pose estimation model usually need be adapted to a novel domain with no labels or sparse labels. Such domain adaptation for 2D pose estimation hasn't been explored. The main reason is that a pose, by nature, has typical topological structure and needs fine-grained features in local keypoints. While existing adaptation methods do not consider topological structure of object-of-interest and they align the whole images coarsely. Therefo…
CILP-FGDI: Exploiting Vision-Language Model for Generalizable Person Re-Identification
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education（教育部新一代人工智能技术及其交叉应用重点实验室（东南大学））
SCMM: Calibrating Cross-modal Representations for Text-Based Person Search
College of Future Information Technology, Fudan University（复旦大学未来信息技术学院）；Department of Electrical and Computer Engineering, The University of British Columbia（不列颠哥伦比亚大学电气与计算机工程系）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；MEGVII Technology（MEGVII技术）；School of Computer Science and Informatics, Cardiff University（卡迪夫大学计算机科学与信息学院）；School of AI and CS, Nantong University（南通大学人工智能与计算机科学学院）；Academy of Artificial Intelligence, SMBU（SMBU人工智能学院）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）
