基于验证器引导伪标签的真实世界点跟踪
Real-World Point Tracking with Verifier-Guided Pseudo-Labeling
用元模型逐帧评估多个预训练跟踪器的预测可靠性来筛选伪标签，这一思路可直接迁移到其他缺乏真值标注的视频任务（如光流、关键点跟踪）的域自适应微调中。
原文依据 a meta-model that learns to assess the reliability of tracker predictions and guide pseudo-label generation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论长时点跟踪模型通常在大型合成数据集上训练。由于真实世界视频具有不同的特性且缺乏稠密真值标注，这些模型在真实视频上的性能会下降。在无标注视频上进行自训练已被探索为一种实用的解决方案，但伪标签的质量在很大程度上取决于教师预测的可靠性，而后者在不同帧和场景间存在波动。本文针对真实世界微调问题，提出验证器（Verifier），一种学习评估跟踪器预测可靠性并引导伪标签生成的元模型。给定来自多个预训练跟踪器的候选轨迹，验证器逐帧对其进行评估，并选出最可信的预测以构建精炼后的伪标签轨迹。在微调过程中应用时，验证器引导的伪标签显著提升了监督质量，并实现了对无标注视频的数据高效自适应。在四个真实世界基准上的大量实验表明，本方法在所需数据少于先前自训练方法的情况下取得了最优结果。
英文原文摘要 1175 字符
Models for long-term point tracking are typically trained on large synthetic datasets. The performance of these models degrades in real-world videos due todifferent 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 predictions, 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 to construct refined pseudo-label trajectories.When applied during fine-tuning, verifier-guided pseudo-labeling substantially improves the quality of supervision and enables data-efficient adaptation to unlabeled videos.Extensive experiments on four real-world benchmarks demonstrate that our approach achieves state-of-the-art results while requiring less data than prior self-training methods.