Fine-Grained Audio–Visual Event Localization

Audio–visual event localization (AVEL) aims to recognize events in videos by associating audio–visual information. However, events involved in existing AVEL tasks are usually coarse-grained events. Actually, finer-grained events are sometimes necessary to be distinguished, especially in certain expert-level applications or rich-content-generation studies. However, this is challenging because they are more difficult to detect or distinguish compared with coarse-grained events. To better address this problem, we discuss a new setting of fine-grained AVEL from dataset to method. First, we constructed the first fine-grained audio–visual event dataset, which is called IT-AVE, relying on videos of playing musical instruments, containing 13k video clips and over 52k audio–visual events. All events are labeled from professional music practitioners, and the event categories are all derived from playing techniques, which are fine-grained with little interclass variation. Next, we designed a new fine-grained event localization method, spatial–temporal video event detector (SVED), which focuses on the challenges that fine-grained events are more imperceptible and prone to be disturbed. Finally, we conduct extensive experiments based on the proposed IT-AVE dataset versus fine-grained versions of two existing related datasets, including UnAV-22 derived from UnAV-100 and FineAction-AV derived from FineAction. Experimental results demonstrate the effectiveness of our method. We hope that this work will contribute to the exploration of an integrated understanding of audio–visual videos.