Distributed Audio-Visual Parsing Based On Multimodal Transformer and Deep Joint Source Channel Coding

Abstract

Audio-visual parsing (AVP) is a newly emerged multimodal perception task, which detects and classifies audio-visual events in video. However, most existing AVP networks only use a simple attention mechanism to guide audio-visual multimodal events, and are implemented in a single end. This makes it unable to effectively capture the relationship between audio-visual events, and is not suitable for implementation in the network transmission scenario. In this paper, we focus on these problems and propose a distributed audio-visual parsing network (DAVPNet) based on multimodal transformer and deep joint source channel coding (DJSCC). Multimodal transformers are used to enhance the attention calculation between audio-visual events, and DJSCC is used to apply DAVP tasks to network transmission scenarios. Finally, the Look, Listen, and Parse (LLP) dataset is used to test the algorithm performance, and the experimental results show that the DAVPNet has superior parsing performance.