SaSR-Net: Source-Aware Semantic Representation Network for Enhancing Audio-Visual Question Answering
Abstract: Audio-Visual Question Answering (AVQA) is a challenging task that involves answering questions based on both auditory and visual information in videos. A significant challenge is interpreting complex multi-modal scenes, which include both visual objects and sound sources, and connecting them to the given question. In this paper, we introduce the Source-aware Semantic Representation Network (SaSR-Net), a novel model designed for AVQA. SaSR-Net utilizes source-wise learnable tokens to efficiently capture and align audio-visual elements with the corresponding question. It streamlines the fusion of audio and visual information using spatial and temporal attention mechanisms to identify answers in multi-modal scenes. Extensive experiments on the Music-AVQA and AVQA datasets show that SaSR-Net outperforms state-of-the-art AVQA methods. We will release our source code and pre-trained models.
Paper Type: Long
Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond
Research Area Keywords: vision question answering, speech and vision, multimodality
Contribution Types: Model analysis & interpretability
Languages Studied: English
Submission Number: 2769
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