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），旨在识别并定位音频/视频模态中的所有事件。先前工作仅关注跨模态的视频级整体标签降噪，却忽视了片段级标签噪声——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，识别片段内的事件颇具挑战性，因其标签可能是视频中出现的任意事件组合。为解决该问题，我们从语言视角处理AVVP，因为语言能够自由描述各片段中不同事件的出现方式，突破固定标签的限制。具体而言，我们设计语言提示以描述每个视频中事件出现的所有情况，随后计算语言提示与片段间的相似度，将最相似提示所对应的事件视为片段级标签。此外，针对误标注片段，我们提出对不可靠片段进行动态加权，以调整其标签。实验表明，我们简洁高效的方法以显著优势超越了现有最优技术。