Data efficient training for egocentric vision-based action recognition
We investigate the application of semi-supervised learning in egocentric action anticipation to tackle the issue of limited labeled data. Leveraging both fully labeled and pseudo-labeled data for training can effectively improve model performance, especially when fully labeled data is scarce. We imp...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2025
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| Online Access: | https://hdl.handle.net/10356/182402 |