2019

See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification

Hu, Tao, Qi, Honggang, Huang, Qingming et al.

Understand

Data augmentation is usually adopted to increase the amount of training data, prevent overfitting and improve the performance of deep models.

However, in practice, random data augmentation, such as random image cropping, is low-efficiency and might introduce many uncontrolled background noises.
In this paper, we propose Weakly Supervised Data Augmentation Network (WS-DAN) to explore the potential of data augmentation.
Specifically, for each training image, we first generate attention maps to represent the object's discriminative parts by weakly supervised learning.

Built on

Novel dataset for fine-grained image categorization: Stanford dogs
A. Khosla, N. Jayadevaprakash, B. Yao, and F.-F. Li · 2011
Earlier work this paper cites.
The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
Earlier work this paper cites.
3d object representations for fine-grained categorization
J. Krause, M. Stark, J. Deng, and L. Fei-Fei · 2013
Earlier work this paper cites.
Network in network
Original
M. Lin, Q. Chen, and S. Yan · 2013
Earlier work this paper cites.
Fine-grained visual classification of aircraft
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi · 2013
Earlier work this paper cites.
Selective search for object recognition
J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders · 2013
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Original
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Part-based r-cnns for fine-grained category detection
N. Zhang, J. Donahue, R. B. Girshick, and T. Darrell · 2014
Earlier work this paper cites.
Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu · 2015
Earlier work this paper cites.
Deep lac: Deep localization, alignment and classification for fine-grained recognition
D. Lin, X. Shen, C. Lu, and J. Jia · 2015
Earlier work this paper cites.
Bilinear cnn models for fine-grained visual recognition
T.-Y. Lin, A. RoyChowdhury, and S. Maji · 2015
Earlier work this paper cites.
Faster R-CNN: towards real-time object detection with region proposal networks
S. Ren, K. He, R. B. Girshick, and J. Sun · 2015
Earlier work this paper cites.
Neural activation constellations: Unsupervised part model discovery with convolutional networks
M. Simon and E. Rodner · 2015
Earlier work this paper cites.

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Then

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