  Audio-visual question answering (AVQA) requires reference to video content
and auditory information, followed by correlating the question to predict the
most precise answer. Although mining deeper layers of audio-visual information
to interact with questions facilitates the multimodal fusion process, the
redundancy of audio-visual parameters tends to reduce the generalization of the
inference engine to multiple question-answer pairs in a single video. Indeed,
the natural heterogeneous relationship between audiovisuals and text makes the
perfect fusion challenging, to prevent high-level audio-visual semantics from
weakening the network's adaptability to diverse question types, we propose a
framework for performing mutual correlation distillation (MCD) to aid question
inference. MCD is divided into three main steps: 1) firstly, the residual
structure is utilized to enhance the audio-visual soft associations based on
self-attention, then key local audio-visual features relevant to the question
context are captured hierarchically by shared aggregators and coupled in the
form of clues with specific question vectors. 2) Secondly, knowledge
distillation is enforced to align audio-visual-text pairs in a shared latent
space to narrow the cross-modal semantic gap. 3) And finally, the audio-visual
dependencies are decoupled by discarding the decision-level integrations. We
evaluate the proposed method on two publicly available datasets containing
multiple question-and-answer pairs, i.e., Music-AVQA and AVQA. Experiments show
that our method outperforms other state-of-the-art methods, and one interesting
finding behind is that removing deep audio-visual features during inference can
effectively mitigate overfitting. The source code is released at
http://github.com/rikeilong/MCD-forAVQA.
