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2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Boston, MA, USA
June 7, 2015 to June 12, 2015
ISSN: 1063-6919
ISBN: 978-1-4673-6963-3
pp: 5546-5555
Jonathan Krause , Stanford University, USA
Hailin Jin , Adobe Research, USA
Jianchao Yang , Adobe Research, USA
Li Fei-Fei , Stanford University, USA
ABSTRACT
Scaling up fine-grained recognition to all domains of fine-grained objects is a challenge the computer vision community will need to face in order to realize its goal of recognizing all object categories. Current state-of-the-art techniques rely heavily upon the use of keypoint or part annotations, but scaling up to hundreds or thousands of domains renders this annotation cost-prohibitive for all but the most important categories. In this work we propose a method for fine-grained recognition that uses no part annotations. Our method is based on generating parts using co-segmentation and alignment, which we combine in a discriminative mixture. Experimental results show its efficacy, demonstrating state-of-the-art results even when compared to methods that use part annotations during training.
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CITATION
Jonathan Krause, Hailin Jin, Jianchao Yang, Li Fei-Fei, "Fine-grained recognition without part annotations", 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 00, no. , pp. 5546-5555, 2015, doi:10.1109/CVPR.2015.7299194
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