Online contextual reasoning and association across consecutive video frames are critical to perceive instances in visual tracking. However, most current top-performing trackers persistently lean on sparse temporal relationships between reference and search frames via an offline mode. Consequently, they can only interact independently within each image-pair and establish limited temporal correlations. To alleviate the above problem, we propose a simple, flexible and effective video-level tracking pipeline, named \textbf{ODTrack}, which densely associates the contextual relationships of video frames in an online token propagation manner. ODTrack receives video frames of arbitrary length to capture the spatio-temporal trajectory relationships of an instance, and compresses the discrimination features (localization information) of a target into a token sequence to achieve frame-to-frame association. This new solution brings the following benefits: 1) the purified token sequences can serve as prompts for the inference in the next video frame, whereby past information is leveraged to guide future inference; 2) the complex online update strategies are effectively avoided by the iterative propagation of token sequences, and thus we can achieve more efficient model representation and computation. ODTrack achieves a new \textit{SOTA} performance on seven benchmarks, while running at real-time speed. Code and models are available at \url{https://github.com/GXNU-ZhongLab/ODTrack}.
Drift-Resilient Temporal Priors for Visual Tracking
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Pengcheng Laboratory（鹏城实验室）；Lero, the Research Ireland Centre for Software, University of Limerick（Lero，爱尔兰软件研究中心，利默里克大学）；Pazhou Lab (Huangpu)（琶洲实验室（黄埔））
UTPTrack: Towards Simple and Unified Token Pruning for Visual Tracking
Institute of Digital Twin, Eastern Institute of Technology（数字孪生研究所，东部技术研究所）；Ningbo Key Laboratory of Spatial Intelligence and Digital Derivative（宁波空间智能与数字衍生关键实验室）；Shanghai Jiao Tong University（上海交通大学）；The Hong Kong Polytechnic University（香港理工大学）；Munich Center for Machine Learning, LMU Munich（慕尼黑机器学习中心，慕尼黑大学）
An Efficient Token Compression Framework for Visual Object Tracking
Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University（教育区块链与智能技术重点实验室，教育部，广西师范大学）；Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University（广西多源信息挖掘与安全重点实验室，广西师范大学）；University Engineering Research Center of Educational Intelligent Technology, Guangxi Normal University（教育智能技术大学工程研究中心，广西师范大学）；Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou University（广西机器视觉与智能控制重点实验室，梧州大学）
TrackingMiM: Efficient Mamba-in-Mamba Serialization for Real-time UAV Object Tracking
Department of Electronic and Electrical Engineering, Southern University of Science and Technology（电子与电气工程系，南方科技大学）；Peng Cheng Laboratory（鹏城实验室）；Department of Applied Mathematics and Theoretical Physics, University of Cambridge（应用数学与理论物理系，剑桥大学）；State Grid Corporation of China（国家电网公司）；East China Institute of Computing Technology（东部计算技术研究所）；Shenzhen Key Laboratory of Robotics and Computer Vision（深圳机器人与计算机视觉重点实验室）
FARTrack: Fast Autoregressive Visual Tracking with High Performance
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件工程学院）；State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University（西安交通大学人机混合增强智能国家重点实验室）；DAMO Academy, Alibaba Group（阿里云达摩院）；National University of Singapore（新加坡国立大学）
SFDATrack: Generalized Source-Free Domain Adaptive Tracking Under Adverse Weather Conditions
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Beijing University of Posts and Telecommunications（北京邮电大学）；Nanyang Technological University（南洋理工大学）
HieDG: A Hierarchical Discrete Geometry-Guided Framework for Multi-Animal Tracking
State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所脑认知与脑启发智能技术国家重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Technology, Beijing Forestry University（北京林业大学工学院）；School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学前沿交叉科学学院）；College of Computer Science, Sichuan University（四川大学计算机学院）；C²DL, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所C²DL）；School of Automation, Chongqing University（重庆大学自动化学院）；Faculty of Life and Health Sciences, Shenzhen University of Advanced Technology（深圳理工大学生命健康学院）；School of Future Technology, University of Chinese Academy of Sciences（中国科学院大学未来技术学院）
MotionTrack: Learning Motion Predictor for Multiple Object Tracking
School of Computer Science, National University of Defense Technology（国防科技大学计算机学院）；JD Explore Academy（京东探索研究院）；Meituan Inc.（美团公司）；Laboratory of Digitizing Software for Frontier Equipment, National University of Defense Technology（国防科技大学前沿装备数字化软件实验室）
Adaptively Bypassing Vision Transformer Blocks for Efficient Visual Tracking
College of Computer Science and Engineering, Guilin University of Technology（桂林理工大学计算机科学与工程学院）；Department of Computer Science and Engineering, Southern University of Science and Technology（南方科技大学计算机科学与工程系）；College of Computer Science, Sichuan University（四川大学计算机学院）
Lost and Found: Overcoming Detector Failures in Online Multi-Object Tracking
Fondazione Bruno Kessler（布鲁诺·凯塞勒基金会）；CiTIUS, Univ. of Santiago de Compostela（西班牙圣地亚哥-德孔波斯特拉大学CiTIUS研究中心）；Inria Grenoble, Univ. Grenoble Alpes（法国国家信息与自动化研究所格勒诺布尔分部、格勒诺布尔阿尔卑斯大学）
MambaEVT: Event Stream based Visual Object Tracking using State Space Model
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；Beijing Institute of Technology（北京理工大学）
Context-Aware Token Pruning and Discriminative Selective Attention for Transformer Tracking
Department of Computational Mathematics, University of Moratuwa（计算数学系，摩图瓦大学）；Department of Interdisciplinary Studies, University of Jaffna（跨学科研究系，贾夫纳大学）；Department of Computer Science, University of Jaffna（计算机科学系，贾夫纳大学）
Learning to Track Instance from Single Nature Language Description
Key Laboratory of Education Blockchain and Intelligent Technology, Ministry of Education Guangxi Normal University（教育区块链与智能技术重点实验室，教育部广西师范大学）；University Engineering Research Center of Educational Intelligent Technology Guangxi Normal University（教育智能技术大学工程研究中心，广西师范大学）；University of Southampton（南安普顿大学）
Long-Term Visual Object Tracking with Event Cameras: An Associative Memory Augmented Tracker and A Benchmark Dataset
School of Computer Science and Technology, Anhui University（计算机科学与技术学院，安徽大学）；School of Electronic and Information Engineering, Anhui University（电子与信息工程学院，安徽大学）
TAPNext: Tracking Any Point (TAP) as Next Token Prediction
Google DeepMind（谷歌DeepMind）；Chandar Research Lab（钱达尔研究实验室）；University College London（伦敦大学学院）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；Polytechnique Montreal（蒙特利尔理工学院）；Université de Montréal（蒙特利尔大学）
Towards Universal Modal Tracking with Online Dense Temporal Token Learning
Key Laboratory of Education Blockchain and Intelligent Technology, Ministry of Education（教育区块链与智能技术重点实验室，教育部）；Guangxi Key Laboratory of Multi-Source Information Mining and Security, Guangxi Normal University（广西多源信息挖掘与安全重点实验室，广西师范大学）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；University of Chinese Academy of Sciences（中国科学院大学）；Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University（媒体分析与计算实验室，人工智能系，厦门大学）
