WILDCAT: Weakly supervised learning of deep convnets for image classification, pointwise localization and segmentation

Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017

Volumn 2017-January, Issue , 2017, Pages 5957-5966

  Durand, Thibaut ^a   Mordan, Taylor ^a,b   Thome, Nicolas ^c   Cord, Matthieu ^a  

^a SORBONNE UNIVERSITÉ   (France)

^b Laser Solutions Unit   (France)

^c University of Paris Cité   (France)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CONVOLUTION; DEEP LEARNING; IMAGE SEGMENTATION; NEURAL NETWORKS; SEMANTICS;

CONVOLUTIONAL NETWORKS; CONVOLUTIONAL NEURAL NETWORK; OBJECT LOCALIZATION; SEMANTIC SEGMENTATION; SPATIAL RESOLUTION; STATE-OF-THE-ART METHODS; WEAKLY SUPERVISED LEARNING; WEAKLY SUPERVISED TRAININGS;

IMAGE CLASSIFICATION;

EID: 85044546504     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2017.631     Document Type: Conference Paper

References (71)

1. R. Arandjelović, P. Gronat, A. Torii, T. Pajdla, and J. Sivic. NetVLAD: CNN architecture for weakly supervised place recognition. In CVPR, 2016.
2. S. Avila, N. Thome, M. Cord, E. Valle, and A. Araujo. Pooling in image representation: the visual codeword point of view. Computer Vision and Image Understanding, 2012.
3. A. Bearman, O. Russakovsky, V. Ferrari, and L. Fei-Fei. What's the Point: Semantic Segmentation with Point Supervision. In ECCV, 2016.
4. H. Ben Younes, R. Cadene, M. Cord, and N. Thome. MU-TAN: Multimodal Tucker Fusion for Visual Question Answering. In arXiv, 2017.
5. A. J. Bency, H. Kwon, H. Lee, S. Karthikeyan, and B. S. Manjunath. Weakly supervised localization using deep feature maps. In ECCV, 2016.
6. H. Bilen and A. Vedaldi. Weakly supervised deep detection networks. In CVPR, 2016.
7. M. Blot, M. Cord, and N. Thome. Max-min convolutional neural networks for image classification. In IEEE International Conference on Image Processing (ICIP), 2016.
8. L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. In ICLR, 2015.
9. J. Dai, K. He, Y. Li, S. Ren, and J. Sun. Instance-sensitive fully convolutional networks. In ECCV, 2016.
10. J. Dai, Y. Li, K. He, and J. Sun. R-FCN: Object detection via region-based fully convolutional networks. In NIPS, 2016.
11. T. G. Dietterich, R. H. Lathrop, and T. Lozano-Pérez. Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell., 1997.
12. T. Durand, N. Thome, and M. Cord. MANTRA: Minimum Maximum Latent Structural SVM for Image Classification and Ranking. In ICCV, 2015.
13. T. Durand, N. Thome, and M. Cord. WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks. In CVPR, 2016.
14. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results. http://www.pascalnetwork.org/challenges/VOC/voc2007/workshop/index.html.
15. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://www.pascalnetwork.org/challenges/VOC/voc2012/workshop/index.html.
16. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ra-manan. Object Detection with Discriminatively Trained Part Based Models. In PAMI, 2010.
17. A. Fukui, D. H. Park, D. Yang, A. Rohrbach, T. Darrell, and M. Rohrbach. Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding. arXiv:1606.01847, 2016.
18. Y. Gao, O. Beijbom, N. Zhang, and T. Darrell. Compact Bilinear Pooling. In CVPR, 2016.
19. S. Gidaris and N. Komodakis. Object detection via a multi-region and semantic segmentation-aware cnn model. In ICCV, 2015.
20. R. Girshick. Fast R-CNN. In ICCV, 2015.
21. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014.
22. R. Girshick, F. Iandola, T. Darrell, and J. Malik. Deformable part models are convolutional neural networks. In CVPR, 2015.
23. G. Gkioxari, R. Girshick, and J. Malik. Contextual action recognition with r* cnn. In CVPR, 2015.
24. H. Goh, N. Thome, M. Cord, and J.-H. Lim. Learning Deep Hierarchical Visual Feature Coding. IEEE Transactions on Neural Networks and Learning Systems, 2014.
25. Y. Gong, L. Wang, R. Guo, and S. Lazebnik. Multi-scale orderless pooling of deep convolutional activation features. In ECCV, 2014.
26. B. Hariharan, P. Arbelaez, L. D. Bourdev, S. Maji, and J. Malik. Semantic contours from inverse detectors. In ICCV, 2011.
27. K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. In ECCV, 2014.
28. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016.
29. M. Jaderberg, K. Simonyan, A. Zisserman, et al. Spatial Transformer Networks. In NIPS, 2015.
30. M. Juneja, A. Vedaldi, C. V. Jawahar, and A. Zisserman. Blocks that shout: Distinctive parts for scene classification. In CVPR, 2013.
31. A. Kolesnikov and C. H. Lampert. Seed, expand and constrain: Three principles for weakly-supervised image segmentation. In ECCV, 2016.
32. P. Krähenbühl and V. Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. In NIPS. 2011.
33. J. Krause, T. Gebru, J. Deng, L.-J. Li, and F.-F. Li. Learning features and parts for fine-grained recognition. In ICPR, 2014.
34. A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS. 2012.
35. P. Kulkarni, F. Jurie, J. Zepeda, P. Pérez, and L. Chevallier. Spleap: Soft pooling of learned parts for image classification. In ECCV, 2016.
36. K.-T. Lai, F. X. Yu, M.-S. Chen, and S.-F. Chang. Video event detection by inferring temporal instance labels. In CVPR, 2014.
37. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, 2006.
38. L.-J. Li, H. Su, E. P. Xing, and L. Fei-Fei. Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification. In NIPS, 2010.
39. W. Li and N. Vasconcelos. Multiple Instance Learning for Soft Bags via Top Instances. In CVPR, 2015.
40. D. Lin, X. Shen, C. Lu, and J. Jia. Deep lac: Deep localization, alignment and classification for fine-grained recognition. In CVPR, 2015.
41. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ra-manan, P. Dollár, and C. L. Zitnick. Microsoft COCO: Common Objects in Context. In ECCV, 2014.
42. J. Long, E. Shelhamer, and T. Darrell. Fully Convolutional Networks for Semantic Segmentation. In CVPR, 2015.
43. M. Oquab, L. Bottou, I. Laptev, and J. Sivic. Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks. In CVPR, 2014.
44. M. Oquab, L. Bottou, I. Laptev, and J. Sivic. Is object localization for free? Weakly-supervised learning with convolutional neural networks. In CVPR, 2015.
45. G. Papandreou, L.-C. Chen, K. Murphy, and A. L. Yuille. Weakly-and semi-supervised learning of a DCNN for semantic image segmentation. In ICCV, 2015.
46. S. N. Parizi, J. G. Oberlin, and P. F. Felzenszwalb. Reconfigurable models for scene recognition. In CVPR, 2012.
47. S. N. Parizi, A. Vedaldi, A. Zisserman, and P. F. Felzenszwalb. Automatic discovery and optimization of parts for image classification. In ICLR, 2015.
48. D. Pathak, P. Krahenbuhl, and T. Darrell. Constrained Convolutional Neural Networks for Weakly Supervised Segmentation. In ICCV, 2015.
49. D. Pathak, E. Shelhamer, J. Long, and T. Darrell. Fully Convolutional Multi-Class Multiple Instance Learning. In ICLR (Workshop), 2015.
50. P. O. Pinheiro and R. Collobert. From image-level to pixel-level labeling with convolutional networks. In CVPR, 2015.
51. A. Quattoni and A. Torralba. Recognizing indoor scenes. In CVPR, 2009.
52. S. Ren, K. He, R. Girshick, and J. Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In NIPS. 2015.
53. 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), 2015.
54. F. Sadeghi and M. F. Tappen. Latent pyramidal regions for recognizing scenes. In ECCV, 2012.
55. W. Shang, K. Sohn, D. Almeida, and H. Lee. Understanding and improving convolutional neural networks via concatenated rectified linear units. In ICML, 2016.
56. K. Simonyan and A. Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. In ICLR, 2015.
57. J. Sivic and A. Zisserman. Video google: A text retrieval approach to object matching in videos. In ICCV, 2003.
58. C. Sun, M. Paluri, R. Collobert, R. Nevatia, and L. Bourdev. ProNet: Learning to Propose Object-Specific Boxes for Cascaded Neural Networks. In CVPR, 2016.
59. J. Sun and J. Ponce. Learning discriminative part detectors for image classification and cosegmentation. In ICCV, 2013.
60. L. Wan, D. Eigen, and R. Fergus. End-to-end integration of a convolution network, deformable parts model and non-maximum suppression. In CVPR, 2015.
61. Z. Wei and M. Hoai. Region Ranking SVM for Image Classification. In CVPR, June 2016.
62. J. Weng, N. Ahuja, and T. S. Huang. Cresceptron: a self-organizing neural network which grows adaptively. In International Joint Conference on Neural Networks (IJCNN), 1992.
63. Xu, Ba, Kiros, Cho, Courville, Salakhutdinov, Zemel, and Bengio. Show, attend and tell: Neural image caption generation with visual attention. In ICML, 2015.
64. H. Xu and K. Saenko. Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering. In B. Leibe, J. Matas, N. Sebe, and M. Welling, editors, ECCV, 2016.
65. F. X. Yu, D. Liu, S. Kumar, T. Jebara, and S.-F. Chang. ∝svm for learning with label proportions. In ICML, 2013.
66. Zhang, Lin, Brandt, Shen, and Sclaroff. Top-down neural attention by excitation backprop. In ECCV, 2016.
67. H. Zhang, T. Xu, M. Elhoseiny, X. Huang, S. Zhang, A. El-gammal, and D. Metaxas. SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-grained Recognition. In CVPR, 2016.
68. N. Zhang, J. Donahue, R. Girshick, and T. Darrell. Part-based r-cnns for fine-grained category detection. In ECCV, 2014.
69. N. Zhang, M. Paluri, M. Ranzato, T. Darrell, and L. Bourdev. PANDA: Pose Aligned Networks for Deep Attribute Modeling. In ECCV, 2014.
70. B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba. Learning Deep Features for Discriminative Localization. In CVPR, 2016.
71. B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. Learning Deep Features for Scene Recognition using Places Database. In NIPS, 2014.