Hyper-Class Augmented and Regularized Deep Learning for Fine-Grained Image Classification

Saining Xie, Tianbao Yang, Xiaoyu Wang, Yuanqing Lin; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 2645-2654

Abstract


Deep convolutional neural networks (CNN) have seen tremendous success in large-scale generic object recognition. In comparison with generic object recognition, fine-grained image classification (FGIC) is much more challenging because (i) fine-grained labeled data is much more expensive to acquire (usually requiring domain expertise); (ii) there exists large intra-class and small inter-class variance. Most recent work exploiting deep CNN for image recognition with small training data adopts a simple strategy: pre-train a deep CNN on a large-scale external dataset (e.g., ImageNet) and fine-tune on the small-scale target data to fit the specific classification task. In this paper, beyond the fine-tuning strategy, we propose a systematic framework of learning a deep CNN that addresses the challenges from two new perspectives: (i) identifying easily annotated hyper-classes inherent in the fine-grained data and acquiring a large number of hyper-class-labeled images from readily available external sources (e.g., image search engines), and formulating the problem into multi-task learning; (ii) a novel learning model by exploiting a regularization between the fine-grained recognition model and the hyper-class recognition model. We demonstrate the success of the proposed framework on two small-scale fine-grained datasets (Stanford Dogs and Stanford Cars) and on a large-scale car dataset that we collected.

Related Material


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[bibtex]
@InProceedings{Xie_2015_CVPR,
author = {Xie, Saining and Yang, Tianbao and Wang, Xiaoyu and Lin, Yuanqing},
title = {Hyper-Class Augmented and Regularized Deep Learning for Fine-Grained Image Classification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}