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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 acrossmodalities, but overlook the segment-level label noise, where adjacent videosegments (i.e., 1-second video clips) may contain different events. However,recognizing events in the segment is challenging because its label could be anycombination of events that occur in the video. To address this issue, weconsider tackling AVVP from the language perspective, since language couldfreely describe how various events appear in each segment beyond fixed labels.Specifically, we design language prompts to describe all cases of eventappearance for each video. Then, the similarity between language prompts andsegments is calculated, where the event of the most similar prompt is regardedas the segment-level label. In addition, to deal with the mislabeled segments,we propose to perform dynamic re-weighting on the unreliable segments to adjusttheir labels. Experiments show that our simple yet effective approachoutperforms state-of-the-art methods by a large margin.

Revisit Weakly-Supervised Audio-Visual Video Parsing from the Language Perspective