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Fine-Grained Categorization & Dataset Bootstrapping Using Deep Metric Learning With Humans in the Loop
CVPR 2016 | We propose an iterative framework for fine-grained categorization and dataset bootstrapping. Using deep metric learning with humans in the loop, we learn a low-dimensional feature embedding with anchor points on manifolds for each category. In each round, images with high confidence scores are sent to humans for labeling, and the model is retrained based on the updated dataset. The proposed framework leads to significant performance gain.
Collaborators: Yin Cui, Feng Zhou, Yuanqing Lin, Serge Belongie
