title: Attention Bottlenecks for Multimodal Fusion
abstract: Humans perceive the world by concurrently processing and fusing high-dimensional inputs from multiple modalities such as vision and audio.  Machine perception models, in stark contrast, are typically modality-specific and optimised for unimodal benchmarks.
  A common approach for building multimodal models is to simply combine multiple of these modality-specific architectures using late-stage fusion of final representations or predictions ('late-fusion').
  Instead, we introduce a novel transformer based architecture that uses 'attention bottlenecks' for modality fusion at multiple layers. Compared to traditional pairwise self-attention,  these bottlenecks force information between different modalities to pass through a small number of '`bottleneck' latent units, requiring the model to collate and condense the most relevant information in each modality and only share what is necessary. We find that such a strategy improves fusion performance, at the same time reducing computational cost. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple audio-visual classification benchmarks including Audioset, Epic-Kitchens and VGGSound. All code and models will be released.
authors: Arsha Nagrani Shan Yang Anurag Arnab Aren Jansen Cordelia Schmid Chen Sun
hIndex: Cordelia Schmid 134