  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.
