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Yuan Gong; Alexander H. Liu; Andrew Rouditchenko; James Glass
Abstract
Conventional audio-visual models have independent audio and video branches. In this work, we unify the audio and visual branches by designing a Unified Audio-Visual Model (UAVM). The UAVM achieves a new state-of-the-art audio-visual event classification accuracy of 65.8% on VGGSound. More interestingly, we also find a few intriguing properties of UAVM that the modality-independent counterparts do not have.
Code Repositories
YuanGongND/uavm
Official
pytorch
Mentioned in GitHub
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| audio-classification-on-audioset | UAVM (Audio + Video) | Test mAP: 0.504 |
| audio-classification-on-vggsound | UAVM (Audio + Video) | Top 1 Accuracy: 65.8 |
| audio-classification-on-vggsound | UAVM (Audio Only) | Top 1 Accuracy: 56.5 |
| audio-classification-on-vggsound | UAVM (Video Only) | Top 1 Accuracy: 49.9 |
| multi-modal-classification-on-audioset | UAVM | Average mAP: 0.504 |
| multi-modal-classification-on-vgg-sound | UAVM | Top-1 Accuracy: 65.8 |
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