Fine-grained categorization and dataset bootstrapping using deep metric learning with humans in the loop
Research output: Contribution to journal › Conference article › Research › peer-review
Existing fine-grained visual categorization methods often suffer from three challenges: lack of training data, large number of fine-grained categories, and high intraclass vs. low inter-class variance. In this work we propose a generic iterative framework for fine-grained categorization and dataset bootstrapping that handles these three challenges. Using deep metric learning with humans in the loop, we learn a low dimensional feature embedding with anchor points on manifolds for each category. These anchor points capture intra-class variances and remain discriminative between classes. In each round, images with high confidence scores from our model are sent to humans for labeling. By comparing with exemplar images, labelers mark each candidate image as either a 'true positive' or a 'false positive.' True positives are added into our current dataset and false positives are regarded as 'hard negatives' for our metric learning model. Then the model is retrained with an expanded dataset and hard negatives for the next round. To demonstrate the effectiveness of the proposed framework, we bootstrap a fine-grained flower dataset with 620 categories from Instagram images. The proposed deep metric learning scheme is evaluated on both our dataset and the CUB-200-2001 Birds dataset. Experimental evaluations show significant performance gain using dataset bootstrapping and demonstrate state-of-the-art results achieved by the proposed deep metric learning methods.
Original language | English |
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Journal | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
Pages (from-to) | 1153-1162 |
Number of pages | 10 |
ISSN | 1063-6919 |
DOIs | |
Publication status | Published - 9 Dec 2016 |
Externally published | Yes |
Event | 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States Duration: 26 Jun 2016 → 1 Jul 2016 |
Conference
Conference | 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 |
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Country | United States |
City | Las Vegas |
Period | 26/06/2016 → 01/07/2016 |
Bibliographical note
Publisher Copyright:
© 2016 IEEE.
ID: 301828374