Semi-Supervised Audio-Visual Video Action Recognition with Audio Source Localization Guided Mixup

• URL: http://arxiv.org/abs/2503.02284v1
• Date: Tue, 04 Mar 2025 05:13:56 GMT
• Title: Semi-Supervised Audio-Visual Video Action Recognition with Audio Source Localization Guided Mixup
• Authors: Seokun Kang, Taehwan Kim,
• Abstract summary: We propose audio-visual SSL for video action recognition, which uses both visual and audio together.<n>In experiments on UCF-51, Kinetics-400, and VGGSound datasets, our model shows the superior performance of the proposed framework.
• Score: 2.80888070977859
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video action recognition is a challenging but important task for understanding and discovering what the video does. However, acquiring annotations for a video is costly, and semi-supervised learning (SSL) has been studied to improve performance even with a small number of labeled data in the task. Prior studies for semi-supervised video action recognition have mostly focused on using single modality - visuals - but the video is multi-modal, so utilizing both visuals and audio would be desirable and improve performance further, which has not been explored well. Therefore, we propose audio-visual SSL for video action recognition, which uses both visual and audio together, even with quite a few labeled data, which is challenging. In addition, to maximize the information of audio and video, we propose a novel audio source localization-guided mixup method that considers inter-modal relations between video and audio modalities. In experiments on UCF-51, Kinetics-400, and VGGSound datasets, our model shows the superior performance of the proposed semi-supervised audio-visual action recognition framework and audio source localization-guided mixup.

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