arXiv AI June 17, 2026 By Shih-Fang Chen, Jun-Cheng Chen, I-Hong Jhuo, Yen-Yu Lin

GOT-JEPA: Generic Object Tracking with Model Adaptation and Occlusion Handling using Joint-Embedding Predictive Architecture

arXiv:2602. 14771v5 Announce Type: replace-cross Abstract: The human visual system tracks objects by integrating current observations with previously observed information, adapting to target and scene changes, and reasoning about occlusion at fine granularity.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 24

Polycepta: Object-Centric Appearance Estimation for Multi-Object Tracking

arXiv:2606. 23604v2 Announce Type: replace-cross Abstract: The tracking-by-detection paradigm in multi-object tracking (MOT) typically relies on static appearance descriptors to complement motion estimation.

By Mohamed Nagy, Naoufel Werghi, Jorge Dias, Majid Khonji

benchmarks

arXiv AI

Jul 3

Rethinking Generic Object Tracking Toward Human-Level Perceptual Intelligence

arXiv:2607. 01395v1 Announce Type: cross Abstract: At the heart of human visual perception lies the ability to maintain a continuous and coherent understanding of the external world.

By Shih-Fang Chen

computer-vision

arXiv Machine Learning

Jul 3

COVTrack++: Learning Open-Vocabulary Multi-Object Tracking from Continuous Videos via a Synergistic Paradigm

arXiv:2603. 24016v2 Announce Type: replace-cross Abstract: Multi-Object Tracking (MOT) has traditionally focused on a few specific categories, restricting its applicability to real-world scenarios involving diverse objects.

By Zekun Qian, Wei Feng, Ruize Han, Junhui Hou

benchmarks

arXiv AI

Jul 21

VLA-ReID: Video-Level Association for Re-Identification in Multi-Object Tracking with Highly Similar Objects

arXiv:2607. 17157v1 Announce Type: cross Abstract: Multi-object tracking (MOT) aims to localize multiple objects in videos while preserving their identities over time.

By Yanrong Qin, Xiaoyan Cao, Yao Yao

benchmarks

arXiv AI

Jun 26

Active Adversarial Perturbation-driven Associative Memory Retrieval for RGB-Event Visual Object Tracking

arXiv:2606. 26455v1 Announce Type: cross Abstract: RGB-Event tracking improves localization robustness by fusing RGB appearance textures and dense temporal motion cues from event sensors.

By Xiao Wang, Xufeng Lou, Zikang Yan, Lan Chen, Sibao Chen, Yaowei Wang, Yonghong Tian, Jin Tang

multimodal safety

arXiv AI

Jul 7

Generative Semantic Multi-Object Tracking: A Large-Scale Benchmark and an MLLM-Driven Reasoning Framework

arXiv:2601. 06550v3 Announce Type: replace-cross Abstract: Semantic Multi-Object Tracking (SMOT) is evolving from purely geometric localization toward comprehensive video understanding.

By Pan Liao, Feng Yang, Di Wu, Jinwen Yu, Wang Zhao, Dingwen Zhang

llms multimodal benchmarks safety

Related stories

Polycepta: Object-Centric Appearance Estimation for Multi-Object Tracking

Rethinking Generic Object Tracking Toward Human-Level Perceptual Intelligence

COVTrack++: Learning Open-Vocabulary Multi-Object Tracking from Continuous Videos via a Synergistic Paradigm

VLA-ReID: Video-Level Association for Re-Identification in Multi-Object Tracking with Highly Similar Objects

Active Adversarial Perturbation-driven Associative Memory Retrieval for RGB-Event Visual Object Tracking

Generative Semantic Multi-Object Tracking: A Large-Scale Benchmark and an MLLM-Driven Reasoning Framework