Pre-training is playing an increasingly important role in learning generic feature representation for Person Re-identification (ReID). We argue that a high-quality ReID representation should have three properties, namely, multi-level awareness, occlusion robustness, and cross-region invariance. To this end, we propose a simple yet effective pre-training framework, namely PersonMAE, which involves two core designs into masked autoencoders to better serve the task of Person Re-ID. 1) PersonMAE generates two regions from the given image with RegionA as the input and \textit{RegionB} as the prediction target. RegionA is corrupted with block-wise masking to mimic common occlusion in ReID and its remaining visible parts are fed into the encoder. 2) Then PersonMAE aims to predict the whole RegionB at both pixel level and semantic feature level. It encourages its pre-trained feature representations with the three properties mentioned above. These properties make PersonMAE compatible with downstream Person ReID tasks, leading to state-of-the-art performance on four downstream ReID tasks, i.e., supervised (holistic and occluded setting), and unsupervised (UDA and USL setting). Notably, on the commonly adopted supervised setting, PersonMAE with ViT-B backbone achieves 79.8% and 69.5% mAP on the MSMT17 and OccDuke datasets, surpassing the previous state-of-the-art by a large margin of +8.0 mAP, and +5.3 mAP, respectively.
DPM++: Dynamic Masked Metric Learning for Occluded Person Re-identification
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学）
SimMC: Simple Masked Contrastive Learning of Skeleton Representations for Unsupervised Person Re-Identification
Nanyang Technological University（南洋理工大学）；Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY)（南洋理工大学-不列颠哥伦比亚大学活跃老年生活卓越联合研究中心（LILY））
Ensemble Learning using Transformers and Convolutional Networks for Masked Face Recognition
King Fahd University of Petroleum and Minerals（法赫德国王石油与矿产大学）；SDAIA-KFUPM Joint Research Center for Artificial Intelligence（沙特数据与人工智能局-法赫德国王石油与矿产大学人工智能联合研究中心）
HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception
Zhejiang University（浙江大学）；Baidu VIS（百度视觉技术部）；University of Science and Technology Beijing（北京科技大学）；South China University of Technology（华南理工大学）；Shanghai Jiao Tong University（上海交通大学）；Jilin University（吉林大学）
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）
Social-MAE: A Transformer-Based Multimodal Autoencoder for Face and Voice
Numediart Institute, ISIA Lab, University of Mons（Numediart研究院、ISIA实验室、蒙斯大学）；Institute for Creative Technologies, University of Southern California（创意技术研究所、南加州大学）
Enhancing Visual Representation for Text-based Person Searching
Metrological Verification and Testing Institute of Jiaxing（嘉兴市计量检定测试所）；School of Artificial Intelligence, Zhejiang Sci-Tech University（浙江理工大学人工智能学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；College of Information Science and Engineering, Jiaxing University（嘉兴学院信息科学与工程学院）
SVFAP: Self-supervised Video Facial Affect Perceiver
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Tsinghua University（清华大学）
Patch Progression Masked Autoencoder with Fusion CNN Network for Classifying Evolution Between Two Pairs of 2D OCT Slices
LaTIM UMR 1101, Inserm（LaTIM UMR 1101，Inserm）；University of Western Brittany（西部布列塔尼大学）；Evolucare Technologies（Evolucare技术公司）；College of Computer Science, Sichuan University（四川大学计算机学院）；United Imaging Healthcare（联合影像医疗）；University of Electronic Science and Technology of China（电子科技大学）；Northwestern University（西北大学）；ShanghaiTech University（上海科技大学）；Wuhan University（武汉大学）；MD Anderson Cancer Center（MD安德森癌症中心）；Guangdong General Hospital（广东人民医院）
Scale-Aware Pre-Training for Human-Centric Visual Perception: Enabling Lightweight and Generalizable Models
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China（深圳先进研究院，电子科学与技术大学）；Center for Future Media, University of Electronic Science and Technology of China（未来媒体中心，电子科学与技术大学）；TongJi University（同济大学）
Automatic Synthetic Data and Fine-grained Adaptive Feature Alignment for Composed Person Retrieval
Beijing University of Posts and Telecommunications（北京邮电大学）；SenseTime（商汤科技）；Beijing Key Laboratory of Network System and Network Culture（北京网络系统与网络文化重点实验室）；Key Laboratory of Intereactive Technology and Experience System, Ministry of Culture and Tourism（文化和旅游部交互技术与体验系统重点实验室）
MMFusion: Multi-modality Diffusion Model for Lymph Node Metastasis Diagnosis in Esophageal Cancer
Shandong University（山东大学）；Hangzhou Dianzi University（杭州电子科技大学）；Communication University of Zhejiang（浙江传媒学院）；University of Leicester（莱斯特大学）；Sichuan Cancer Hospital and Institution（四川省肿瘤医院）；Sichuan Cancer Center（四川省癌症中心）；University of Electronic Science and Technology of China（电子科技大学）；Suzhou Research Institute of Shandong University（山东大学苏州研究院）
