Projects | Fine-Grained Image Classification by Exploring Bipartite-Graph Labels

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Fine-Grained Image Classification by Exploring Bipartite-Graph Labels

CVPR 2016 | We exploit the rich relationships among fine-grained classes for fine-grained image classification. We model the relations using the proposed bipartite-graph labels (BGL) and incorporate them into CNN training. Our system is computationally efficient in inference thanks to the bipartite structure. We also construct a new food benchmark dataset consisting of 37,885 food images collected from six restaurants and 975 menus.

Collaborators: Feng Zhou, Yuanqing Lin

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Fine-Grained Image Classification by Exploring Bipartite-Graph Labels

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Projects | Fine-Grained Image Classification by Exploring Bipartite-Graph Labels

Fine-Grained Image Classification by Exploring Bipartite-Graph Labels

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