CAD -- Contextual Multi-modal Alignment for Dynamic AVQA

• URL: http://arxiv.org/abs/2310.16754v2
• Date: Fri, 27 Oct 2023 11:36:47 GMT
• Title: CAD -- Contextual Multi-modal Alignment for Dynamic AVQA
• Authors: Asmar Nadeem, Adrian Hilton, Robert Dawes, Graham Thomas, Armin Mustafa
• Abstract summary: Existing AVQA methods suffer from two major shortcomings. The proposed novel CAD network improves the overall performance over the state-of-the-art methods on average by 9.4%.
• Score: 20.155816093525374
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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