AnchorNet: A weakly supervised network to learn geometry-sensitive features for semantic matching
Despite significant progress of deep learning in recent years, state-of-the-art semantic matching methods still rely on legacy features such as SIFT or HoG. We argue that the strong invariance properties that are key to the success of recent deep architectures on the classification task make them un...
| Auteurs principaux: | , , |
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| Format: | Conference item |
| Publié: |
Institute of Electrical and Electronics Engineers
2017
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