  In the context of Audio Visual Question Answering (AVQA) tasks, the audio
visual modalities could be learnt on three levels: 1) Spatial, 2) Temporal, and
3) Semantic. Existing AVQA methods suffer from two major shortcomings; the
audio-visual (AV) information passing through the network isn't aligned on
Spatial and Temporal levels; and, inter-modal (audio and visual) Semantic
information is often not balanced within a context; this results in poor
performance. In this paper, we propose a novel end-to-end Contextual
Multi-modal Alignment (CAD) network that addresses the challenges in AVQA
methods by i) introducing a parameter-free stochastic Contextual block that
ensures robust audio and visual alignment on the Spatial level; ii) proposing a
pre-training technique for dynamic audio and visual alignment on Temporal level
in a self-supervised setting, and iii) introducing a cross-attention mechanism
to balance audio and visual information on Semantic level. The proposed novel
CAD network improves the overall performance over the state-of-the-art methods
on average by 9.4% on the MUSIC-AVQA dataset. We also demonstrate that our
proposed contributions to AVQA can be added to the existing methods to improve
their performance without additional complexity requirements.
