A dual-stage self-supervised tracker learns robust representations by first using semantic prompts on forward and backward branches then injecting contextual noise to handle complex feature spaces.
Towards more flexible and accurate object tracking with natural language: Algo- rithms and benchmark
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Boosting Self-Supervised Tracking with Contextual Prompts and Noise Learning
A dual-stage self-supervised tracker learns robust representations by first using semantic prompts on forward and backward branches then injecting contextual noise to handle complex feature spaces.