We focus on the weakly-supervised audio-visual video parsing task (AVVP), which aims to identify and locate all the events in audio/visual modalities. Previous works only concentrate on video-level overall label denoising across modalities, but overlook the segment-level label noise, where adjacent video segments (i.e., 1-second video clips) may contain different events. However, recognizing events in the segment is challenging because its label could be any combination of events that occur in the video. To address this issue, we consider tackling AVVP from the language perspective, since language could freely describe how various events appear in each segment beyond fixed labels. Specifically, we design language prompts to describe all cases of event appearance for each video. Then, the similarity between language prompts and segments is calculated, where the event of the most similar prompt is regarded as the segment-level label. In addition, to deal with the mislabeled segments, we propose to perform dynamic re-weighting on the unreliable segments to adjust their labels. Experiments show that our simple yet effective approach outperforms state-of-the-art methods by a large margin.

翻译：我们聚焦于弱监督音视频解析任务（AVVP），该任务旨在识别并定位音频/视觉模态中的所有事件。以往研究仅关注模态间视频级整体标签的去噪，却忽略了片段级标签噪声——相邻视频片段（即1秒长的视频剪辑）可能包含不同事件。然而，由于每个片段的标签可能是视频中出现的任意事件组合，识别片段中的事件具有挑战性。为解决该问题，我们考虑从语言视角处理AVVP，因为语言能自由描述每个片段中各类事件的呈现方式，而不仅限于固定标签。具体而言，我们设计语言提示来描述每个视频所有可能的事件出现情况。随后，计算语言提示与片段之间的相似度，并将最相似提示对应的事件视为该片段的标签。此外，为处理错误标记的片段，我们提出对不可靠片段进行动态重加权以调整其标签。实验表明，我们简单而有效的方法以较大优势超越了当前最先进的方法。