Methods › Computer Vision › Multi-Modal Methods › AVSlowFast

Audiovisual SlowFast Network

AVSlowFast

1 paper tagged archive 2025-07-28

Introduced by Fanyi Xiao et al. in Audiovisual SlowFast Networks for Video Recognition

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Audiovisual SlowFast Network, or AVSlowFast, is an architecture for integrated audiovisual perception. AVSlowFast has Slow and Fast visual pathways that are integrated with a Faster Audio pathway to model vision and sound in a unified representation. Audio and visual features are fused 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, DropPathway is used, which randomly drops the Audio pathway during training as an effective regularization technique. Inspired by prior studies in neuroscience, hierarchical audiovisual synchronization is performed to learn joint audiovisual features.

PaperSource

Papers archive 2025-07-28

1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

2 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Action Classification1
Video Recognition1

Usage over time archive 2025-07-28

Papers per year tagged with AVSlowFast: 2020 to 2020, peak 1 1 0 2020: 1 paper 2020
Papers per year the archive tags with this method, by the paper's archive date (1 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Multi-Modal MethodsVideo Recognition Models

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