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.
Correlation-aware deep tracking
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.