Learning the structure of object categories from incomplete supervision
<p>This thesis aims at learning and predicting the fine-grained structure of visual object categories given input image data. Alleviating the common requirement of collecting an ample amount of manual annotations, we propose several approaches that learn given an incomplete supervisory signal....
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| Format: | Thesis |
| Language: | English |
| Published: |
2018
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