基于句子移除的上下文一致性学习用于半监督视频段落定位
Context Consistency Learning via Sentence Removal for Semi-Supervised Video Paragraph Grounding
AI总结：
针对半监督视频段落定位，提出上下文一致性学习框架，通过句子移除增强和伪标签置信度提升性能，大幅超越现有方法。
摘要：
半监督视频段落定位（SSVPG）旨在利用有限的时序标注，从未修剪的视频中定位段落中的多个句子。现有方法侧重于师生一致性学习和视频级对比损失，但忽略了扰动查询上下文以生成强监督信号的重要性。在这项工作中，我们提出了一种新颖的上下文一致性学习（CCL）框架，该框架统一了一致性正则化和伪标签生成的范式，以增强半监督学习。具体来说，我们首先进行师生学习，其中学生模型以移除句子的强增强样本作为输入，并被强制从教师模型提供的充分强的监督信号中学习。之后，我们基于生成的伪标签进行模型重训练，其中原始视图和增强视图预测之间的相互一致性被用作标签置信度。大量实验表明，CCL在性能上大幅优于现有方法。
英文摘要：
Semi-Supervised Video Paragraph Grounding (SSVPG) aims to localize multiple sentences in a paragraph from an untrimmed video with limited temporal annotations. Existing methods focus on teacher-student consistency learning and video-level contrastive loss, but they overlook the importance of perturbing query contexts to generate strong supervisory signals. In this work, we propose a novel Context Consistency Learning (CCL) framework that unifies the paradigms of consistency regularization and pseudo-labeling to enhance semi-supervised learning. Specifically, we first conduct teacher-student learning where the student model takes as inputs strongly-augmented samples with sentences removed and is enforced to learn from the adequately strong supervisory signals from the teacher model. Afterward, we conduct model retraining based on the generated pseudo labels, where the mutual agreement between the original and augmented views' predictions is utilized as the label confidence. Extensive experiments show that CCL outperforms existing methods by a large margin.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
