We present UniTrack, a plug-and-play graph-theoretic loss function designed to significantly enhance multi-object tracking (MOT) performance by directly optimizing tracking-specific objectives through unified differentiable learning. Unlike prior graph-based MOT methods that redesign tracking architectures, UniTrack provides a universal training objective that integrates detection accuracy, identity preservation, and spatiotemporal consistency into a single end-to-end trainable loss function, enabling seamless integration with existing MOT systems without architectural modifications. Through differentiable graph representation learning, UniTrack enables networks to learn holistic representations of motion continuity and identity relationships across frames. We validate UniTrack across diverse tracking models and multiple challenging benchmarks, demonstrating consistent improvements across all tested architectures and datasets including Trackformer, MOTR, FairMOT, ByteTrack, GTR, and MOTE. Extensive evaluations show up to 53\% reduction in identity switches and 12\% IDF1 improvements across challenging benchmarks, with GTR achieving peak performance gains of 9.7\% MOTA on SportsMOT.
Light Field Based 6DoF Tracking of Previously Unobserved Objects
University of Sydney（悉尼大学）；Australian Centre For Robotics (ACFR)（澳大利亚机器人中心）；School of Aerospace, Mechanical and Mechatronic Engineering（航空航天、机械与机电工程学院）
Learning a Neural Association Network for Self-supervised Multi-Object Tracking
University of Bonn（波恩大学）；Monash University（莫纳什大学）；University of Edinburgh（爱丁堡大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔人工智能与机器学习研究所）
NSFlow: End-to-End Differentiable Neuro-Symbolic Optical Flow for Visual Odometry
State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology（华中科技大学机械科学与工程学院智能制造装备与技术国家重点实验室）
DIMM: Decoupled Multi-hierarchy Kalman Filter for 3D Object Tracking
Shenzhen International Graudate School Tsinghua University（清华大学深圳国际研究生院）；The Hong Kong University of Science and Technology (Guang Zhou)（香港科学与技术大学（广州））；Department of Electronic Engineering Tsinghua University（清华大学电子工程系）
Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking
Institute of Automation Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；TuSimple（图森未来）；School of Artificial Intelligence University of Chinese Academy of Sciences (UCAS)（中国科学院大学人工智能学院）；Centre for Artificial Intelligence and Robotics HKISI_CAS（香港中科院人工智能与机器人中心）
Unsupervised Learning of Accurate Siamese Tracking
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；SenseTime Research（商汤科技研究院）；The University of Sydney（悉尼大学）；Peng Cheng Laboratory（鹏城实验室）；Shanghai AI Laboratory（上海人工智能实验室）
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers
Academy of Military Medical Sciences（军事医学科学院）；National University of Defense Technology（国防科技大学）；University College London（伦敦大学学院）；The Alan Turing Institute（艾伦·图灵研究所）
CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds
Peking University（北京大学）；Stanford University（斯坦福大学）；Shandong University（山东大学）；UCSD（加州大学圣地亚哥分校）；Tencent AI Lab（腾讯AI实验室）；Beijing Institute for General AI（北京通用人工智能研究院）
Dyna3DGR: 4D Cardiac Motion Tracking with Dynamic 3D Gaussian Representation
School of Biomedical Engineering, Division of Life Sciences；Medicine, University of Science；Technology of China (USTC), Hefei Anhui, 230026, China Center for Medical Imaging, Robotics, Analytic Computing \& Learning (MIRACLE), Suzhou Institute for Advance Research, USTC, Suzhou Jiangsu, 215123, China Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology, Suzhou Jiangsu, 215123, China State Key Laboratory of Precision；Intelligent Chemistry, USTC, Hefei Anhui, 230026, China Center of Interventional Radiology \& Vascular Surgery, Department of Radiology, Medical School, Zhongda Hospital, Southeast University, Nanjing 210009, China
