Advancing Weakly-Supervised Audio-Visual Video Parsing via Segment-wise Pseudo Labeling

TL;DR

This paper introduces a novel pseudo label generation method for weakly-supervised audio-visual video parsing, leveraging large-scale pretrained models to improve event localization accuracy across audio, visual, and combined streams.

Contribution

It proposes a segment-wise pseudo label generation strategy using CLIP and CLAP models, along with a new loss function and label denoising to enhance weakly-supervised video parsing performance.

Findings

Achieves state-of-the-art results on LLP dataset

Effective pseudo labels improve event localization accuracy

Method generalizes well to related audio-visual localization tasks

Abstract

The Audio-Visual Video Parsing task aims to identify and temporally localize the events that occur in either or both the audio and visual streams of audible videos. It often performs in a weakly-supervised manner, where only video event labels are provided, \ie, the modalities and the timestamps of the labels are unknown. Due to the lack of densely annotated labels, recent work attempts to leverage pseudo labels to enrich the supervision. A commonly used strategy is to generate pseudo labels by categorizing the known video event labels for each modality. However, the labels are still confined to the video level, and the temporal boundaries of events remain unlabeled. In this paper, we propose a new pseudo label generation strategy that can explicitly assign labels to each video segment by utilizing prior knowledge learned from the open world. Specifically, we exploit the large-scale pretrained models, namely CLIP and CLAP, to estimate the events in each video segment and generate segment-level visual and audio pseudo labels, respectively. We then propose a new loss function to exploit these pseudo labels by taking into account their category-richness and segment-richness. A label denoising strategy is also adopted to further improve the visual pseudo labels by flipping them whenever abnormally large forward losses occur. We perform extensive experiments on the LLP dataset and demonstrate the effectiveness of each proposed design and we achieve state-of-the-art video parsing performance on all types of event parsing, \ie, audio event, visual event, and audio-visual event. We also examine the proposed pseudo label generation strategy on a relevant weakly-supervised audio-visual event localization task and the experimental results again verify the benefits and generalization of our method.