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About Learning Visual Voice Activity Detection With An Automatically Annotated Dataset
The document proposes two deep learning models for visual voice activity detection (V-VAD) that use facial landmarks and optical flow as inputs. It also introduces a new methodology to automatically generate and annotate large V-VAD datasets "in-the-wild" by combining audio VAD with face detection and tracking. The models are evaluated on this new dataset and are shown to outperform previous V-VAD methods, even with the noisy automatic annotations of the large dataset compared to a smaller manually annotated dataset.
Detailed Information
| Author: | ['Abdullah AlGhamdi'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 6 |
| Language: | English |
| Format: | |
| Price: | FREE |
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