CVPR 2016 | We model the multi-level relevance among fine-grained classes for fine-grained categorization. We jointly optimize classification and similarity constraints in a proposed multi-task learning framework, and we embed label structures such as hierarchy or shared attributes into the framework by generalizing the triplet loss. This significantly outperforms previous fine-grained feature representations for image retrieval at different levels of relevance.
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