Real-World Point Tracking with Verifier-Guided Pseudo-Labeling
G\"orkay Aydemir, Fatma G\"uney, Weidi Xie

TL;DR
This paper introduces a verifier-guided pseudo-labeling method for long-term point tracking that improves real-world performance by assessing and selecting reliable predictions for self-training, leading to state-of-the-art results.
Contribution
The paper proposes a verifier meta-model that evaluates tracker predictions to generate high-quality pseudo-labels for effective real-world fine-tuning.
Findings
Achieves state-of-the-art results on four real-world benchmarks.
Requires less data than previous self-training methods.
Significantly improves tracking accuracy in real-world videos.
Abstract
Models for long-term point tracking are typically trained on large synthetic datasets. The performance of these models degrades in real-world videos due to different characteristics and the absence of dense ground-truth annotations. Self-training on unlabeled videos has been explored as a practical solution, but the quality of pseudo-labels strongly depends on the reliability of teacher models, which vary across frames and scenes. In this paper, we address the problem of real-world fine-tuning and introduce verifier, a meta-model that learns to assess the reliability of tracker predictions and guide pseudo-label generation. Given candidate trajectories from multiple pretrained trackers, the verifier evaluates them per frame and selects the most trustworthy predictions, resulting in high-quality pseudo-label trajectories. When applied for fine-tuning, verifier-guided pseudo-labeling…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Face recognition and analysis · Human Pose and Action Recognition
