SatSAM2 improves satellite video object tracking by integrating Kalman motion priors and a motion state machine into SAM2, outperforming prior methods with a 5.84% AUC gain on OOTB while introducing the MVOT synthetic benchmark.
Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick
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CLDyN establishes a closed-loop semantic transmission chain with a Requirement-driven Semantic Compensation module to make infrared-visible fusion adapt to diverse downstream tasks.
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SatSAM2: Motion-Constrained Video Object Tracking in Satellite Imagery using Promptable SAM2 and Kalman Priors
SatSAM2 improves satellite video object tracking by integrating Kalman motion priors and a motion state machine into SAM2, outperforming prior methods with a 5.84% AUC gain on OOTB while introducing the MVOT synthetic benchmark.
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Customized Fusion: A Closed-Loop Dynamic Network for Adaptive Multi-Task-Aware Infrared-Visible Image Fusion
CLDyN establishes a closed-loop semantic transmission chain with a Requirement-driven Semantic Compensation module to make infrared-visible fusion adapt to diverse downstream tasks.