SAMOSA adapts SAM 2 for complex visual object tracking by integrating explicit nonlinear motion prediction, semantic cues for failure recovery, and geometric constraints for stability, outperforming prior SAM 2-based and supervised methods on benchmarks including anti-UAV datasets.
Mambavt: Spatio-temporal contextual modeling for robust rgb-t tracking
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Segment Anything with Motion, Geometry, and Semantic Adaptation for Complex Nonlinear Visual Object Tracking
SAMOSA adapts SAM 2 for complex visual object tracking by integrating explicit nonlinear motion prediction, semantic cues for failure recovery, and geometric constraints for stability, outperforming prior SAM 2-based and supervised methods on benchmarks including anti-UAV datasets.