Look, Listen, and Answer: Overcoming Biases for Audio-Visual Question Answering
<|reference_start|>Look, Listen, and Answer: Overcoming Biases for Audio-Visual Question Answering: Audio-Visual Question Answering (AVQA) is a complex multi-modal reasoning task, demanding intelligent systems to accurately respond to natural language queries based on audio-video input pairs. Nevertheless, prevalent AVQA approaches are prone to overlearning dataset biases, resulting in poor robustness. Furthermore, current datasets may not provide a precise diagnostic for these methods. To tackle these challenges, firstly, we propose a novel dataset, \textit{MUSIC-AVQA-R}, crafted in two steps: rephrasing questions within the test split of a public dataset (\textit{MUSIC-AVQA}) and subsequently introducing distribution shifts to split questions. The former leads to a large, diverse test space, while the latter results in a comprehensive robustness evaluation on rare, frequent, and overall questions. Secondly, we propose a robust architecture that utilizes a multifaceted cycle collaborative debiasing strategy to overcome bias learning. Experimental results show that this architecture achieves state-of-the-art performance on both datasets, especially obtaining a significant improvement of 9.32\% on the proposed dataset. Extensive ablation experiments are conducted on these two datasets to validate the effectiveness of the debiasing strategy. Additionally, we highlight the limited robustness of existing multi-modal QA methods through the evaluation on our dataset.<|reference_end|>
@article{ma2024look,,
    title={Look, Listen, and Answer: Overcoming Biases for Audio-Visual Question
  Answering},
    author={Jie Ma, Min Hu, Pinghui Wang, Wangchun Sun, Lingyun Song, Hongbin Pei,
  Jun Liu, Youtian Du},
    journal={arXiv preprint arXiv:2404.12020},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12020},
    primaryClass={cs.CV}
}