Deep Metric Learning with Angular Loss

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2017-October, Issue , 2017, Pages 2612-2620

  Wang, Jian ^a   Zhou, Feng ^a   Wen, Shilei ^a   Liu, Xiao ^a   Lin, Yuanqing ^a  

^a BAIDU INC   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; SEARCH ENGINES; SEMANTICS;

CONVENTIONAL METHODS; GEOMETRIC CONSTRAINT; HIGH-LEVEL ABSTRACTION; IMAGE SEARCH SYSTEM; PERFORMANCE GAIN; SCALE INVARIANCE; SEMANTIC UNDERSTANDING; SIMILARITY METRICS;

DEEP LEARNING;

EID: 85041913456     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2017.283     Document Type: Conference Paper

References (38)

1. S. Bell and K. Bala. Learning visual similarity for product design with convolutional neural networks. ACM Trans. Graph., 34 (4):98:1-98:10, 2015.
2. K. Bhatia, H. Jain, P. Kar, M. Varma, and P. Jain. Sparse local embeddings for extreme multi-label classification. In NIPS, pages 730-738, 2015.
3. S. Branson, G. V. Horn, C. Wah, P. Perona, and S. Belongie. The ignorant led by the blind: A hybrid human-machine vi-sion system for fine-grained categorization. Int. J. Comput. Vis., 108 (1-2):3-29, 2014.
4. J. Bromley, I. Guyon, Y. LeCun, E. Säckinger, and R. Shah. Signature verification using a Siamese time delay neural network. In NIPS, 1993.
5. G. Chechik, V. Sharma, U. Shalit, and S. Bengio. Large scale online learning of image similarity through ranking. Journal of Machine Learning Research, 11:1109-1135, 2010.
6. S. Chopra, R. Hadsell, and Y. LeCun. Learning a similarity metric discriminatively, with application to face verification. In CVPR, 2005.
7. C. B. Choy, J. Gwak, S. Savarese, and M. K. Chandraker. Universal correspondence network. In NIPS, 2016.
8. Y. Cui, F. Zhou, Y. Lin, and S. J. Belongie. Fine-grained categorization and dataset bootstrapping using deep metric learning with humans in the loop. In CVPR, 2016.
9. O. Duchenne, F. R. Bach, I. Kweon, and J. Ponce. A tensorbased algorithm for high-order graph matching. IEEE Trans. Pattern Anal. Mach. Intell., 33 (12):2383-2395, 2011.
10. A. Fix, A. Gruber, E. Boros, and R. Zabih. A graph cut algorithm for higher-order markov random fields. In ICCV, pages 1020-1027, 2011.
11. A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean, M. Ranzato, and T. Mikolov. DeViSE: A deep visualsemantic embedding model. In NIPS, 2013.
12. J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov. Neighborhood components analysis. In NIPS, 2004.
13. C.-K. Hsieh, L. Yang, Y. Cui, T.-Y. Lin, S. Belongie, and D. Estrin. Collaborative metric learning. In WWW, 2017.
14. C. Huang, C. C. Loy, and X. Tang. Local similarity-aware deep feature embedding. In NIPS, pages 1262-1270, 2016.
15. H. Jégou, M. Douze, and C. Schmid. Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell., 33 (1):117-128, 2011.
16. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. B. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. In ACM MM, pages 675-678, 2014.
17. M. H. Kiapour, X. Han, S. Lazebnik, A. C. Berg, and T. L. Berg. Where to buy it: Matching street clothing photos in online shops. In ICCV, 2015.
18. J. Krause, M. Stark, J. Deng, and L. Fei-Fei. 3D object representations for fine-grained categorization. In ICCV Workshop on 3D Representation and Recognition, 2013.
19. B. Kulis. Metric learning: A survey. Foundations and Trends in Machine Learning, 5 (4):287-364, 2013.
20. Y. Li, H. Su, C. R. Qi, N. Fish, D. Cohen-Or, and L. J. Guibas. Joint embeddings of shapes and images via CNN image purification. ACM Trans. Graph., 34 (6):234:1-234:12, 2015.
21. O. Rippel, M. Paluri, P. Dollar, and L. Bourdev. Metric learning with adaptive density discrimination. In CVPR, 2015.
22. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. S. Bernstein, A. C. Berg, and F. Li. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115 (3):211-252, 2015.
23. R. Salakhutdinov and G. Hinton. Learning a nonlinear embedding by preserving class neighbourhood structure. In AISTATS, 2007.
24. F. Schroff, D. Kalenichenko, and J. Philbin. FaceNet: A unified embedding for face recognition and clustering. In CVPR, 2015.
25. K. Sohn. Improved deep metric learning with multi-class N-pair loss objective. In NIPS, 2016.
26. H. Song, Y. Xiang, S. Jegelka, and S. Savarese. Deep metric learning via lifted structured feature embedding. In CVPR, 2016.
27. H. O. Song, S. Jegelka, V. Rathod, and K. Murphy. Learnable structured clustering framework for deep metric learning. CoRR, abs/1612. 01213, 2016.
28. Y. Sun, Y. Chen, X. Wang, and X. Tang. Deep learning face representation by joint identification-verification. In NIPS, 2014.
29. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In CVPR, 2015.
30. Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. DeepFace: Closing the gap to human-level performance in face verification. In CVPR, 2014.
31. E. Ustinova and V. S. Lempitsky. Learning deep embeddings with histogram loss. In NIPS, pages 4170-4178, 2016.
32. L. van der Maaten. Accelerating t-SNE using treebased algorithms. Journal of Machine Learning Research, 15 (1):3221-3245, 2014.
33. J. Wang, Y. Song, T. Leung, C. Rosenberg, J. Wang, J. Philbin, B. Chen, and Y. Wu. Learning fine-grained image similarity with deep ranking. In CVPR, 2014.
34. K. Weinberger and L. Saul. Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 10:207-244, 2009.
35. J. Weston, S. Bengio, and N. Usunier. WSABIE: scaling up to large vocabulary image annotation. In IJCAI, pages 2764-2770, 2011.
36. D. Yi, Z. Lei, and S. Z. Li. Deep metric learning for practical person re-identification. CoRR, abs/1407. 4979, 2014.
37. Y. Yuan, K. Yang, and C. Zhang. Hard-aware deeply cascaded embedding. CoRR, abs/1611. 05720, 2016.
38. X. Zhang, F. Zhou, Y. Lin, and S. Zhang. Embedding label structures for fine-grained feature representation. In CVPR, 2016.