  We present Audiovisual SlowFast Networks, an architecture for integrated
audiovisual perception. AVSlowFast has Slow and Fast visual pathways that are
deeply integrated with a Faster Audio pathway to model vision and sound in a
unified representation. We fuse audio and visual features at multiple layers,
enabling audio to contribute to the formation of hierarchical audiovisual
concepts. To overcome training difficulties that arise from different learning
dynamics for audio and visual modalities, we introduce DropPathway, which
randomly drops the Audio pathway during training as an effective regularization
technique. Inspired by prior studies in neuroscience, we perform hierarchical
audiovisual synchronization to learn joint audiovisual features. We report
state-of-the-art results on six video action classification and detection
datasets, perform detailed ablation studies, and show the generalization of
AVSlowFast to learn self-supervised audiovisual features. Code will be made
available at: https://github.com/facebookresearch/SlowFast.
