Learning deep embeddings with histogram loss

Advances in Neural Information Processing Systems

Volumn , Issue , 2016, Pages 4177-4185

  Ustinova, Evgeniya ^a   Lempitsky, Victor ^a  

^a SKOLKOVO INSTITUTE OF SCIENCE AND TECHNOLOGY   (Russian Federation)

Author keywords

[No Author keywords available]

Indexed keywords

GRAPHIC METHODS; OPTIMIZATION;

1D-HISTOGRAMS; EMBEDDINGS; PIECE-WISE; SIMILARITY DISTRIBUTION; SIMILARITY SCORES; STOCHASTIC OPTIMIZATIONS;

PROBABILITY DISTRIBUTIONS;

EID: 85019265802     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (31)

1. R. Arandjelović, P. Gronat, A. Torii, T. Pajdla, and J. Sivic. Netvlad: Cnn architecture for weakly supervised place recognition. IEEE International Conference on Computer Vision, 2015.
2. A. Bowman and A. Azzalini. Applied smoothing techniques for data analysis. Number 18 in Oxford statistical science series. Clarendon Press, Oxford, 1997.
3. J. Bromley, J. W. Bentz, L. Bottou, I. Guyon, Y. LeCun, C. Moore, E. Säckinger, and R. Shah. Signature verification using a "siamese" time delay neural network. International Journal of Pattern Recognition and Artificial Intelligence, 7(04): 669-688, 1993.
4. G. Chechik, V. Sharma, U. Shalit, and S. Bengio. Large scale online learning of image similarity through ranking. The Journal of Machine Learning Research, 11: 1109-1135, 2010.
5. S. Chopra, R. Hadsell, and Y. LeCun. Learning a similarity metric discriminatively, with application to face verification. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), 20-26 June 2005, San Diego, CA, USA, pp. 539-546, 2005.
6. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv: 1408.5093, 2014.
7. D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2014.
8. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems (NIPS), pp. 1097-1105, 2012.
9. M. Law, N. Thome, and M. Cord. Quadruplet-wise image similarity learning. Proceedings of the IEEE International Conference on Computer Vision, pp. 249-256, 2013.
10. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4): 541-551, 1989.
11. W. Li, R. Zhao, T. Xiao, and X. Wang. Deepreid: Deep filter pairing neural network for person reidentification. 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, June 23-28, 2014, pp. 152-159, 2014.
12. J. Lin, O. Morère, V. Chandrasekhar, A. Veillard, and H. Goh. Deephash: Getting regularization, depth and fine-tuning right. CoRR, abs/1501.04711, 2015.
13. O. M. Parkhi, A. Vedaldi, and A. Zisserman. Deep face recognition. Proceedings of the British Machine Vision Conference 2015, BMVC 2015, Swansea, UK, September 7-10, 2015, pp. 41.1-41.12, 2015.
14. Q. Qian, R. Jin, S. Zhu, and Y. Lin. Fine-grained visual categorization via multi-stage metric learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3716-3724, 2015.
15. A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson. CNN features off-the-shelf: An astounding baseline for recognition. IEEE Conference on Computer Vision and Pattern Recognition, CVPR Workshops 2014, Columbus, OH, USA, June 23-28, 2014, pp. 512-519, 2014.
16. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3): 211-252, 2015.
17. F. Schroff, D. Kalenichenko, and J. Philbin. Facenet: A unified embedding for face recognition and clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815-823, 2015.
18. F. Schroff, D. Kalenichenko, and J. Philbin. Facenet: A unified embedding for face recognition and clustering. IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015, pp. 815-823, 2015.
19. M. Schultz and T. Joachims. Learning a distance metric from relative comparisons. Advances in neural information processing systems (NIPS), p. 41, 2004.
20. E. Simo-Serra, E. Trulls, L. Ferraz, I. Kokkinos, P. Fua, and F. Moreno-Noguer. Discriminative learning of deep convolutional feature point descriptors. Proceedings of the IEEE International Conference on Computer Vision, pp. 118-126, 2015.
21. H. O. Song, Y. Xiang, S. Jegelka, and S. Savarese. Deep metric learning via lifted structured feature embedding. Computer Vision and Pattern Recognition (CVPR), 2016.
22. Y. Sun, Y. Chen, X. Wang, and X. Tang. Deep learning face representation by joint identification-verification. Advances in Neural Information Processing Systems, pp. 1988-1996, 2014.
23. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, 2015.
24. O. Tadmor, T. Rosenwein, S. Shalev-Shwartz, Y. Wexler, and A. Shashua. Learning a metric embedding for face recognition using the multibatch method. NIPS, 2016.
25. Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Deepface: Closing the gap to human-level performance in face verification. Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pp. 1701-1708. IEEE, 2014.
26. C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. The Caltech-UCSD Birds-200-2011 Dataset. (CNS-TR-2011-001), 2011.
27. K. Q. Weinberger and L. K. Saul. Distance metric learning for large margin nearest neighbor classification. The Journal of Machine Learning Research, 10: 207-244, 2009.
28. D. Yi, Z. Lei, and S. Z. Li. Deep metric learning for practical person re-identification. arXiv prepzrint arXiv: 1407.4979, 2014.
29. J. Žbontar and Y. LeCun. Stereo matching by training a convolutional neural network to compare image patches. arXiv preprint arXiv: 1510.05970, 2015.
30. L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, and Q. Tian. Scalable person re-identification: A benchmark. Computer Vision, IEEE International Conference on, 2015.
31. W.-S. Zheng, S. Gong, and T. Xiang. Reidentification by relative distance comparison. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(3): 653-668, 2013.