Slim DensePose: Thrifty learning from sparse annotations and motion cues
DensePose supersedes traditional landmark detectors by densely mapping image pixels to body surface coordinates. This power, however, comes at a greatly increased annotation time, as supervising the model requires to manually label hundreds of points per pose instance. In this work, we thus seek met...
| Главные авторы: | , , , , |
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| Формат: | Conference item |
| Язык: | English |
| Опубликовано: |
IEEE
2020
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