Tracker is a self-supervised VL tracker that uses a Dynamic Token Aggregation Module to learn instance tracking from single language descriptions in unlabeled videos and outperforms prior self-supervised methods.
Leveraging local and global cues for visual tracking via parallel interaction network
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3roles
background 2polarities
background 2representative citing papers
ETCTrack compresses template tokens by 60% in visual trackers via an adaptive compressor and hierarchical interaction, cutting MACs 21.4% with 0.4% accuracy drop on seven benchmarks.
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.
citing papers explorer
-
Learning to Track Instance from Single Nature Language Description
Tracker is a self-supervised VL tracker that uses a Dynamic Token Aggregation Module to learn instance tracking from single language descriptions in unlabeled videos and outperforms prior self-supervised methods.
-
An Efficient Token Compression Framework for Visual Object Tracking
ETCTrack compresses template tokens by 60% in visual trackers via an adaptive compressor and hierarchical interaction, cutting MACs 21.4% with 0.4% accuracy drop on seven benchmarks.
-
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.