research-article

Leveraging Fine-Grained Labels to Regularize Fine-Grained Visual Classification

Authors:

Zheyuan DingAuthors Info & Claims

ICCMS '19: Proceedings of the 11th International Conference on Computer Modeling and Simulation

Pages 133 - 136

https://doi.org/10.1145/3307363.3307382

Published: 16 January 2019 Publication History

Abstract

Fine-grained visual categorization (FGVC) is challenging mainly due to the large intra-class confusion and small inter-class variance in terms of shape, pose, and appearance. We propose the concept of fine-grained label and that any given label can be further classified into some sub-classes as fine-grained labels, and thus samples of each original label are classifed into several sub-classes in which only more familiar samples are given the same fine-grained label. The samples of fine-grained labels have less intra-class confusion and bigger inter-class variance. Besides, fine-grained labels can be obtained through unsupervised means without any domain knowledge or annotations. Instead of training on the fine-grained labels directly, we utilize these "free" labels as an auxiliary task to regularize the training of the deep learning model. In the test phase, as sub-classes of the original label, the predicted fine-grained labels are used for integration with original labels to get the final classification results. Experiments on the popular CUB-200-2011 dataset demonstrate that employing the proposed fine-grained labels in CNN model improves performance from both training and test phases.

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Cited By

Peng JWang YZhou Z(2022)Progressive Erasing Network with consistency loss for fine-grained visual classificationJournal of Visual Communication and Image Representation10.1016/j.jvcir.2022.10357087(103570)Online publication date: Aug-2022
https://doi.org/10.1016/j.jvcir.2022.103570
Hu YLiu XZhang BHan JCao X(2021)Alignment Enhancement Network for Fine-grained Visual CategorizationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/344620817:1s(1-20)Online publication date: 31-Mar-2021
https://dl.acm.org/doi/10.1145/3446208
Chang DPang KZheng YMa ZSong YGuo J(2021)Your “Flamingo” is My “Bird”: Fine-Grained, or Not2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR46437.2021.01131(11471-11480)Online publication date: Jun-2021
https://doi.org/10.1109/CVPR46437.2021.01131

Index Terms

Leveraging Fine-Grained Labels to Regularize Fine-Grained Visual Classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Multi-task learning

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ICCMS '19: Proceedings of the 11th International Conference on Computer Modeling and Simulation

January 2019

253 pages

ISBN:9781450366199

DOI:10.1145/3307363

Copyright © 2019 ACM.

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University of Wollongong, Australia
College of Technology Management, National Tsing Hua University, Taiwan
Swinburne University of Technology
University of Technology Sydney

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 January 2019

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ICCMS 2019

ICCMS 2019: The 11th International Conference on Computer Modeling and Simulation

January 16 - 19, 2019

QLD, North Rockhampton, Australia

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Cited By

Peng JWang YZhou Z(2022)Progressive Erasing Network with consistency loss for fine-grained visual classificationJournal of Visual Communication and Image Representation10.1016/j.jvcir.2022.10357087(103570)Online publication date: Aug-2022
https://doi.org/10.1016/j.jvcir.2022.103570
Hu YLiu XZhang BHan JCao X(2021)Alignment Enhancement Network for Fine-grained Visual CategorizationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/344620817:1s(1-20)Online publication date: 31-Mar-2021
https://dl.acm.org/doi/10.1145/3446208
Chang DPang KZheng YMa ZSong YGuo J(2021)Your “Flamingo” is My “Bird”: Fine-Grained, or Not2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR46437.2021.01131(11471-11480)Online publication date: Jun-2021
https://doi.org/10.1109/CVPR46437.2021.01131

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