Fetching the paper…
Reading the bibliography…
Fine-grained visual categorization (FGVC) aims to discriminate similar subcategories, whose main challenge is the large intraclass diversities and subtle inter-class differences.
N. Tishby, F. C. Pereira, and W. Bialek, “The information bottleneck method,” in Proc. of the 37-th Annual Allerton Conference on Communication, Control and Computing , 1999, pp. 368–377
1999
Earlier work this paper cites.
O. e. a. Shamir, “Learning and generalization with the information bottleneck,” Theoretical Computer Science , vol. 411, no. 29-30, pp. 2696–2711, 2010
2010
Earlier work this paper cites.
B.-S. e. a. Wah, Catherine, “The caltech-ucsd birds-200-2011 dataset,” 2011
2011
Earlier work this paper cites.
e. a. Khosla, Aditya, “Novel dataset for fine-grained image categorization: Stanford dogs,” in Proc. CVPR Workshop on FGVC , vol. 2, no. 1. Citeseer, 2011
2011
Earlier work this paper cites.
N. Zhang, J. Donahue, R. Girshick, and T. Darrell, “Part-based r-cnns for fine-grained category detection,” in European conference on computer vision . Springer, 2014, pp. 834–849
2014
Earlier work this paper cites.
L. G. S. Giraldo, M. Rao, and J. C. Principe, “Measures of entropy from data using infinitely divisible kernels,” IEEE Transactions on Information Theory , vol. 61, no. 1, pp. 535–548, 2014
2014
Earlier work this paper cites.
N. Tishby and N. Zaslavsky, “Deep learning and the information bottleneck principle,” in 2015 ieee information theory workshop (itw) . IEEE, 2015, pp. 1–5
2015
Earlier work this paper cites.
G. Van Horn and e. a. Branson, Steve, “Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection,” in CVPR , 2015, pp. 595–604
2015
Earlier work this paper cites.
X. Zhang, H. Xiong, W. Zhou, W. Lin, and Q. Tian, “Picking neural activations for fine-grained recognition,” IEEE Transactions on Multimedia , vol. 19, no. 12, pp. 2736–2750, 2017
2017
Earlier work this paper cites.
B. Zhao, X. Wu, J. Feng, Q. Peng, and S. Yan, “Diversified visual attention networks for fine-grained object classification,” IEEE Transactions on Multimedia , vol. 19, no. 6, pp. 1245–1256, 2017
2017
Earlier work this paper cites.
H. Zheng, J. Fu, T. Mei, and J. Luo, “Learning multi-attention convolutional neural network for fine-grained image recognition,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 5209–5217
2017
Earlier work this paper cites.
J. Fu, H. Zheng, and T. Mei, “Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 4438–4446
2017
Earlier work this paper cites.
F. Wang, M. Jiang, C. Qian, S. Yang, C. Li, H. Zhang, X. Wang, and X. Tang, “Residual attention network for image classification,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 3156–3164
2017
Earlier work this paper cites.
Y. Cui, F. Zhou, J. Wang, X. Liu, Y. Lin, and S. Belongie, “Kernel pooling for convolutional neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2921–2930
2017
Earlier work this paper cites.
T.-Y. Lin, A. RoyChowdhury, and S. Maji, “Bilinear convolutional neural networks for fine-grained visual recognition,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 6, pp. 1309–1322, 2017
2017
Earlier work this paper cites.
A. A. Alemi, I. Fischer, J. V. Dillon, and K. Murphy, “Deep variational information bottleneck,” in International Conference on Learning Representations , 2017
2017
Earlier work this paper cites.
e. a. Cai, Sijia, “Higher-order integration of hierarchical convolutional activations for fine-grained visual categorization,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2017, pp. 511–520
2017
Earlier work this paper cites.
Z. Yang, T. Luo, D. Wang, Z. Hu, J. Gao, and L. Wang, “Learning to navigate for fine-grained classification,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 420–435
2018
Cited alongside, same era.
A. Achille and S. Soatto, “Emergence of invariance and disentanglement in deep representations,” The Journal of Machine Learning Research , vol. 19, no. 1, pp. 1947–1980, 2018
2018
Cited alongside, same era.
M. I. Belghazi, A. Baratin, S. Rajeshwar, S. Ozair, Y. Bengio, A. Courville, and D. Hjelm, “Mutual information neural estimation,” in International conference on machine learning . PMLR, 2018, pp. 531–540
2018
Cited alongside, same era.
Y. Cui, Y. Song, C. Sun, A. Howard, and S. Belongie, “Large scale fine-grained categorization and domain-specific transfer learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4109–4118
2018
Cited alongside, same era.
D. Y. e. a. Chang, Dongliang, “The devil is in the channels: Mutual-channel loss for fine-grained image classification,” IEEE Transactions on Image Processing , vol. 29, pp. 4683–4695, 2020
2020
Later among the works it cites.
S. Min, H. Yao, H. Xie, Z.-J. Zha, and Y. Zhang, “Multi-objective matrix normalization for fine-grained visual recognition,” IEEE Transactions on Image Processing , vol. 29, pp. 4996–5009, 2020
2020
Later among the works it cites.
W. Luo, H. Zhang, J. Li, and X.-S. Wei, “Learning semantically enhanced feature for fine-grained image classification,” IEEE Signal Processing Letters , vol. 27, pp. 1545–1549, 2020
2020
Later among the works it cites.
P. Zhuang, Y. Wang, and Y. Qiao, “Learning attentive pairwise interaction for fine-grained classification,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, 2020, pp. 13 130–13 137
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
C. Liu, H. Xie, Z. Zha, L. Yu, Z. Chen, and Y. Zhang, “Bidirectional attention-recognition model for fine-grained object classification,” IEEE Transactions on Multimedia , vol. 22, no. 7, pp. 1785–1795, 2019
2019
Cited alongside, same era.
Y. Zhang, K. Jia, and Z. Wang, “Part-aware fine-grained object categorization using weakly supervised part detection network,” IEEE Transactions on Multimedia , vol. 22, no. 5, pp. 1345–1357, 2019
2019
Cited alongside, same era.
W. Luo, X. Yang, X. Mo, Y. Lu, L. S. Davis, J. Li, J. Yang, and S.-N. Lim, “Cross-x learning for fine-grained visual categorization,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 8242–8251
2019
Cited alongside, same era.
Y. Ding, Y. Zhou, Y. Zhu, Q. Ye, and J. Jiao, “Selective sparse sampling for fine-grained image recognition,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 6599–6608
2019
Cited alongside, same era.
R. A. Amjad and B. C. Geiger, “Learning representations for neural network-based classification using the information bottleneck principle,” IEEE transactions on pattern analysis and machine intelligence , vol. 42, no. 9, pp. 2225–2239, 2019
2019
Cited alongside, same era.
A. Kolchinsky, B. D. Tracey, and D. H. Wolpert, “Nonlinear information bottleneck,” Entropy , vol. 21, no. 12, p. 1181, 2019
2019
Cited alongside, same era.
A. M. Saxe, Y. Bansal, J. Dapello, M. Advani, A. Kolchinsky, B. D. Tracey, and D. D. Cox, “On the information bottleneck theory of deep learning,” Journal of Statistical Mechanics: Theory and Experiment , vol. 2019, no. 12, p. 124020, 2019
2019
Cited alongside, same era.
J. Han, X. Yao, G. Cheng, X. Feng, and D. Xu, “P-cnn: Part-based convolutional neural networks for fine-grained visual categorization,” IEEE transactions on pattern analysis and machine intelligence , 2019
2019
Cited alongside, same era.
X.-S. Wei, Y.-Z. Song, O. Mac Aodha, J. Wu, Y. Peng, J. Tang, J. Yang, and S. Belongie, “Fine-grained image analysis with deep learning: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021
2021
Later among the works it cites.
Z. Miao, X. Zhao, J. Wang, Y. Li, and H. Li, “Complemental attention multi-feature fusion network for fine-grained classification,” IEEE Signal Processing Letters , vol. 28, pp. 1983–1987, 2021
2021
Later among the works it cites.
Z. Li, Z. Chen, F. Yang, W. Li, Y. Zhu, C. Zhao, R. Deng, L. Wu, R. Zhao, M. Tang et al. , “Mst: Masked self-supervised transformer for visual representation,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Y. Hu, X. Jin, Y. Zhang, H. Hong, J. Zhang, Y. He, and H. Xue, “Rams-trans: Recurrent attention multi-scale transformer for fine-grained image recognition,” in Proceedings of the 29th ACM International Conference on Multimedia , 2021, pp. 4239–4248
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
S. Bang, P. Xie, H. Lee, W. Wu, and E. Xing, “Explaining a black-box by using a deep variational information bottleneck approach,” in AAAI , vol. 35, no. 13, 2021, pp. 11 396–11 404
2021
Later among the works it cites.
2021
Later among the works it cites.
X. Yu, S. Yu, and J. C. Príncipe, “Deep deterministic information bottleneck with matrix-based entropy functional,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 3160–3164
2021
Later among the works it cites.
K. Ahuja, E. Caballero, D. Zhang, J.-C. Gagnon-Audet, Y. Bengio, I. Mitliagkas, and I. Rish, “Invariance principle meets information bottleneck for out-of-distribution generalization,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
Later among the works it cites.