GDSTrack combines modality-guided dynamic graph fusion with temporal graph-informed diffusion to train a self-supervised RGB-T tracker, outperforming prior self-supervised methods on multiple benchmarks.
Scene-dependent pre- diction in latent space for video anomaly detection and anticipation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(1):224–239,
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Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T Tracking
GDSTrack combines modality-guided dynamic graph fusion with temporal graph-informed diffusion to train a self-supervised RGB-T tracker, outperforming prior self-supervised methods on multiple benchmarks.