Papers › Slow-Fast Auditory Streams For Audio Recognition
Slow-Fast Auditory Streams For Audio Recognition
Evangelos Kazakos, Arsha Nagrani, Andrew Zisserman, Dima Damen
We propose a two-stream convolutional network for audio recognition, that operates on time-frequency spectrogram inputs. Following similar success in visual recognition, we learn Slow-Fast auditory streams with separable convolutions and multi-level lateral connections. The Slow pathway has high channel capacity while the Fast pathway operates at a fine-grained temporal resolution. We showcase the importance of our two-stream proposal on two diverse datasets: VGG-Sound and EPIC-KITCHENS-100, and achieve state-of-the-art results on both.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Human Interaction Recognition | EPIC-SOUNDS | Slow-Fast(Finetune by Fivewin team) | Top-1 accuracy % | 55.11 | #1 of 1 | Archive leaderboard | report |
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