弱监督时序动作定位：通过推断显著片段特征
Weakly-Supervised Temporal Action Localization by Inferring Salient Snippet-Feature
AI总结：
提出通过推断显著片段特征生成高保真伪标签的弱监督时序动作定位方法，在THUMOS14和ActivityNet v1.3上超越现有技术。
摘要：
弱监督时序动作定位旨在仅以视频级标签作为监督，同时在未修剪视频中定位动作区域并识别动作类别。伪标签生成是解决这一挑战性问题的有前景的策略，但现有方法忽略了视频中自然的时序结构，而该结构能够提供丰富信息以辅助伪标签生成过程。本文提出了一种新颖的弱监督时序动作定位方法，通过推断显著片段特征来实现。首先，我们设计了一个显著性推断模块，利用时序相邻片段之间的变化关系来发现显著片段特征，这些特征能够反映视频中显著的动态变化。其次，我们引入了一个边界细化模块，通过信息交互单元增强显著片段特征。然后，引入了一个判别增强模块来增强片段特征的判别性。最后，我们采用细化后的片段特征生成高保真度的伪标签，这些伪标签可用于监督动作定位网络的训练。在两个公开数据集（即THUMOS14和ActivityNet v1.3）上进行的大量实验表明，与最先进的方法相比，我们提出的方法取得了显著的改进。
英文摘要：
Weakly-supervised temporal action localization aims to locate action regions and identify action categories in untrimmed videos simultaneously by taking only video-level labels as the supervision. Pseudo label generation is a promising strategy to solve the challenging problem, but the current methods ignore the natural temporal structure of the video that can provide rich information to assist such a generation process. In this paper, we propose a novel weakly-supervised temporal action localization method by inferring salient snippet-feature. First, we design a saliency inference module that exploits the variation relationship between temporal neighbor snippets to discover salient snippet-features, which can reflect the significant dynamic change in the video. Secondly, we introduce a boundary refinement module that enhances salient snippet-features through the information interaction unit. Then, a discrimination enhancement module is introduced to enhance the discriminative nature of snippet-features. Finally, we adopt the refined snippet-features to produce high-fidelity pseudo labels, which could be used to supervise the training of the action localization network. Extensive experiments on two publicly available datasets, i.e., THUMOS14 and ActivityNet v1.3, demonstrate our proposed method achieves significant improvements compared to the state-of-the-art methods.
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。
