Local-to-global Cross-modal Coordination for Self-supervised RGB-T Tracking

Self-supervised RGB-T tracking seeks to learn robust cross-modal representations without dense manual annotations. A commonsupervision strategy relies on forward-backward tracking loops, but inpractice, it is highly vulnerable to error accumulation and modality driftcaused by the inconsistent reliability of visible and thermal cues. To ad-dress this issue, we propose LGCTrack, a self-supervised RGB-T trackerunderpinned by a local-to-global coordination strategy within a closed-loop verification paradigm. At the local level, a hierarchical coordina-tion module performs layer-wise, reliability-aware token interaction toenable adaptive cross-modal fusion. At the global level, a turning-phasecoordination module refines the target representation during the criticalforward-to-backward transition, anchoring target semantics and improv-ing backward tracking. By jointly coordinating cross-modal interactionat two granularities, LGCTrack intrinsically filters pseudo-label noise andmitigates drift. Experiments on standard RGB-T tracking benchmarksshow that LGCTrack consistently outperforms existing self-supervisedmethods and substantially narrows the gap to fully supervised trackers.Our code is available at https://github.com/zyyyya/LGCTrack.