  In recent years, there has been a growing emphasis on the intersection of
audio, vision, and text modalities, driving forward the advancements in
multimodal research. However, strong bias that exists in any modality can lead
to the model neglecting the others. Consequently, the model's ability to
effectively reason across these diverse modalities is compromised, impeding
further advancement. In this paper, we meticulously review each question type
from the original dataset, selecting those with pronounced answer biases. To
counter these biases, we gather complementary videos and questions, ensuring
that no answers have outstanding skewed distribution. In particular, for binary
questions, we strive to ensure that both answers are almost uniformly spread
within each question category. As a result, we construct a new dataset, named
MUSIC-AVQA v2.0, which is more challenging and we believe could better foster
the progress of AVQA task. Furthermore, we present a novel baseline model that
delves deeper into the audio-visual-text interrelation. On MUSIC-AVQA v2.0,
this model surpasses all the existing benchmarks, improving accuracy by 2% on
MUSIC-AVQA v2.0, setting a new state-of-the-art performance.
