Hugging Face Trending Papers August 18, 2026

Probing Association Instability with Track-State Perturbations for Clip-Level Active Learning in Query-Propagation Multi-Object Tracking

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Training query-propagation end-to-end multi-object tracking (MOT) models requires dense bounding-box and identity annotations across video sequences, making dataset construction expensive. Clip-level active learning reduces this cost by selecting video clips for annotation, but prior acquisition criteria based on output-level temporal uncertainty may miss clips whose informativeness comes from association instability in propagated track states.

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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 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 Computer Vision

Sep 22

Beyond the Survey: A Systematic Empirical Study of Detection and Association in Visual MOT

This paper conducts a systematic empirical study of multi‑object tracking (MOT) algorithms, focusing on how detection and association components affect overall performance. By evaluating state‑of‑the‑art methods on benchmarks such as MOT16/17/20, SportsMOT, DanceTrack, and CrowdTrack, the authors find that detection quality has a far greater impact than association strategies, and that transformer‑based end‑to‑end models are more robust to detection variations but computationally expensive. The study provides a unified pipeline diagram and practical guidance for researchers and practitioners in selecting and designing MOT systems.

By Linh Van Ma, Juhua Hu, Wei Cheng, Unse Fatima, Moongu Jeon

llms rag computer-vision benchmarks

arXiv Computer Vision

Aug 31

TQD-Track: Temporal Query Denoising for 3D Multi-Object Tracking

TQD-Track introduces Temporal Query Denoising (TQD) for 3D Multi‑Object Tracking, extending query denoising beyond single frames by initializing denoising queries from previous‑frame ground truths and propagating them as independent association candidates. The method enriches track queries with temporal context and instance‑specific features, and incorporates diverse noise types to emulate real‑world tracking challenges. Experiments on nuScenes and Argoverse 2 show consistent improvements across multiple MOT baselines with only training‑process modifications.

By Yutong Yang, Shuxiao Ding, Mohammed Amine Bencheikh Lehocine, Julian Wiederer, Markus Braun, Peizheng Li, Juergen Gall, Bin Yang

Hugging Face Trending Papers

Sep 23

LiAM-SAM: Lifecycle-Aware Memory for Robust SAM2-Based MOT

Segmentation-based multi-object tracking (MOT) with foundation video models such as SAM2 offers strong localization quality, yet remains fragile in crowded, real-world scenes. In detector-prompted SAM...

computer-vision efficiency 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