SPDA-CNN: Unifying semantic part detection and abstraction for fine-grained recognition

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 2016-December, Issue , 2016, Pages 1143-1152

  Zhang, Han ^a   Xu, Tao ^b   Elhoseiny, Mohamed ^a   Huang, Xiaolei ^b   Zhang, Shaoting ^c   Elgammal, Ahmed ^a   Metaxas, Dimitris ^a  

^a RUTGERS UNIVERSITY   (United States)

^b Lehigh University   (United States)

^c University of North Carolina at Charlotte   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ABSTRACTING; COMPUTER VISION; CONVOLUTION; NETWORK ARCHITECTURE; NEURAL NETWORKS; OBJECT RECOGNITION; SEMANTICS;

CONVOLUTIONAL NEURAL NETWORK; END-TO-END NETWORK; LARGE MARGINS; MODEL SEMANTICS; PARTS DETECTIONS; PROPOSED ARCHITECTURES; SEMANTIC PARTS; STATE OF THE ART;

PATTERN RECOGNITION;

EID: 84986309458     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.129     Document Type: Conference Paper

References (43)

1. P. A. Arbeláez, J. Pont-Tuset, J. T. Barron, F. Marqués, and J. Malik. Multiscale combinatorial grouping. In CVPR, 2014.
2. H. Azizpour and I. Laptev. Object detection using stronglysupervised deformable part models. In ECCV, 2012.
3. T. Berg and P. N. Belhumeur. POOF: part-based one-vs.-one features for fine-grained categorization, face verification, and attribute estimation. In CVPR, 2013.
4. L. D. Bourdev and J. Malik. Poselets: Body part detectors trained using 3d human pose annotations. In ICCV, 2009.
5. S. Branson, G. Van Horn, P. Perona, and S. Belongie. Improved bird species recognition using pose normalized deep convolutional nets. In BMVC, 2014.
6. J. Carreira and C. Sminchisescu. CPMC: Automatic object segmentation using constrained parametric min-cuts. TPAMI, 2012.
7. Y. Chai, V. S. Lempitsky, and A. Zisserman. Symbiotic segmentation and part localization for fine-grained categorization. In ICCV, 2013.
8. J. Dai, K. He, and J. Sun. Convolutional feature masking for joint object and stuff segmentation. In CVPR, 2015.
9. J. Deng, J. Krause, and F. Li. Fine-grained crowdsourcing for fine-grained recognition. In CVPR, 2013.
10. R. Farrell, O. Oza, N. Zhang, V. I. Morariu, T. Darrell, and L. S. Davis. Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance. In ICCV, 2011.
11. P. F. Felzenszwalb, R. B. Girshick, D. A. McAllester, and D. Ramanan. Object detection with discriminatively trained part-based models. TPAMI, 2010.
12. E. Gavves, B. Fernando, C. G. M. Snoek, A. W. M. Smeulders, and T. Tuytelaars. Fine-grained categorization by alignments. In ICCV, 2013.
13. R. B. Girshick. Fast R-CNN. In ICCV, 2015.
14. R. B. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014.
15. G. Gkioxari, R. Girshick, and J. Malik. Actions and attributes from wholes and parts. In ICCV, 2015.
16. C. Göring, E. Rodner, A. Freytag, and J. Denzler. Nonparametric part transfer for fine-grained recognition. In CVPR, 2014.
17. K. Gregor and Y. LeCun. Emergence of complex-like cells in a temporal product network with local receptive fields. CoRR, abs/1006. 0448, 2010.
18. B. Hariharan, P. A. Arbeláez, R. B. Girshick, and J. Malik. Hypercolumns for object segmentation and fine-grained localization. In CVPR, 2015.
19. K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. TPAMI, 2015.
20. G. B. Huang, H. Lee, and E. G. Learned-Miller. Learning hierarchical representations for face verification with convolutional deep belief networks. In CVPR, 2012.
21. M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu. Spatial transformer networks. In NIPS, 2015.
22. 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.
23. A. Khosla, N. Jayadevaprakash, B. Yao, and L. Fei-Fei. Novel dataset for fine-grained image categorization. In CVPR Workshop, 2011.
24. J. Krause, T. Gebru, J. Deng, L. Li, and F. Li. Learning features and parts for fine-grained recognition. In ICPR, 2014.
25. J. Krause, H. Jin, J. Yang, and F. Li. Fine-grained recognition without part annotations. In CVPR, 2015.
26. J. Krause, M. Stark, J. Deng, and L. Fei-Fei. 3D object representations for fine-grained categorization. In ICCV Workshop, 2013.
27. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
28. D. Lin, X. Shen, C. Lu, and J. Jia. Deep LAC: Deep localization, alignment and classification for fine-grained recognition. In CVPR, 2015.
29. T.-Y. Lin, A. RoyChowdhury, and S. Maji. Bilinear cnn models for fine-grained visual recognition. In ICCV, 2015.
30. Y.-L. Lin, V. I. Morariu, W. Hsu, and L. S. Davis. Jointly optimizing 3d model fitting and fine-grained classification. In ECCV, 2014.
31. S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi. Fine-grained visual classification of aircraft. Technical report, 2013.
32. S. Ren, K. He, R. Girshick, and J. Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. In NIPS, 2015.
33. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. In ICLR, 2014.
34. M. Simon and E. Rodner. Neural activation constellations: Unsupervised part model discovery with convolutional networks. In ICCV, 2015.
35. Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. DeepFace: Closing the gap to human-level performance in face verification. In CVPR, 2014.
36. J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders. Selective search for object recognition. IJCV, 2013.
37. C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. The Caltech-UCSD Birds-200-2011 Dataset. Technical report, 2011.
38. P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona. Caltech-UCSD Birds 200. Technical report, 2010.
39. T. Xiao, Y. Xu, K. Yang, J. Zhang, Y. Peng, and Z. Zhang. The application of two-level attention models in deep convolutional neural network for fine-grained image classification. In CVPR, 2015.
40. M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In ECCV, 2014.
41. N. Zhang, J. Donahue, R. B. Girshick, and T. Darrell. Part-based R-CNNs for fine-grained category detection. In ECCV, 2014.
42. N. Zhang, R. Farrell, F. N. Iandola, and T. Darrell. Deformable part descriptors for fine-grained recognition and attribute prediction. In ICCV, 2013.
43. C. L. Zitnick and P. Dollár. Edge boxes: Locating object proposals from edges. In ECCV, 2014.