부산대학교 도서관

As an important and challenging task in video understanding, Temporal Action Detection (TAD) has been deeply studied in recent years. However, current works mainly tackle this task with visual information, while neglecting to explore the potential of the audio modality. To address this challenge, in this paper, we propose a simple yet effective AudioVisual Temporal Action Detection Transformer named AV- TAD, which performs early fusion on audio and visual modalities in an end-to-end fashion. On top of it, a novel query formulation is introduced by directly adopting temporal segment coordinates as queries in Transformer decoder, thus allowing us to perform dynamic segment update layer-by-layer. To the best of our knowledge, this is the first attempt to investigate both audio and video feature with a multi-modal Transformer in TAD task. Extensive experiments on THUMOS14 dataset demonstrate that our proposed AV-TAD can outperform the previous methods by a clear margin.