arXiv Computer Vision
Fed-MAPTrack: Federated Modality-aware Prompt Tuning for RGB-X Tracking
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arXiv Computer Vision
Oct 12
UniMergeTrack: Hierarchical Model Merging for General RGB-X Visual Object Tracking
UniMergeTrack introduces a hierarchical model merging strategy for RGB-X visual object tracking, combining independently trained modality-specific trackers into a unified framework. The method merges transferable backbones using high‑pass‑filtered merging, aligns tracking heads with optimal transport‑guided channel alignment, and fine‑tunes with distribution alignment to integrate cross‑task knowledge while preserving modality cues. Experiments on multiple benchmarks show state‑of‑the‑art or competitive performance across RGB‑Thermal, RGB‑Event, and RGB‑Depth tracking tasks, with strong generalization to unseen datasets.
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.
arXiv Computer Vision
Sep 30
End-to-End Self-Supervised RGB-T Tracking without Modality Misleading
ESMTrack is a fully end‑to‑end self‑supervised RGB‑T tracking framework that eliminates the need for costly modality‑aligned bounding boxes or offline pseudo‑label generation. It learns discriminative, temporally consistent representations using a grounding triplet loss on the initial annotated frame and a cross‑frame temporal triplet loss on unlabeled search frames, with reliable samples selected via forward‑backward consistency. A three‑branch architecture (fusion, RGB, thermal) and a modality decoupling mechanism mitigate modality dominance bias, enabling competitive state‑of‑the‑art performance, strong cross‑dataset generalization, and real‑time inference on five RGB‑T benchmarks.
arXiv Computer Vision
Aug 24
Optimizing Multi-Modality Trackers via Significance-Regularized Tuning
arXiv:2508.17488v4 Announce Type: replace Abstract: This paper tackles the critical challenge of optimizing multi-modality trackers by effectively adapting pre-trained models for RGB data. Existing f...
arXiv AI
Jun 15
FEMOT: Multi-Object Tracking using Frame and Event Cameras
arXiv:2606. 14094v1 Announce Type: cross Abstract: Conventional RGB cameras have been widely used in multi-object tracking due to their ability to capture rich appearance and semantic information.
arXiv Computer Vision
Oct 19
Reliability-Consistent Appearance Modeling for RGB-Infrared Multi-Object Tracking
arXiv:2610.23125v1 Announce Type: new Abstract: Recent RGB-infrared multi-object tracking (MOT) methods have substantially improved cross-modal representation learning and multimodal information inte...