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Augmented Feedback in Semantic Segmentation Under Image Level Supervision

  • Xiaojuan Qi
  • Zhengzhe Liu
  • Jianping Shi
  • Hengshuang Zhao
  • Jiaya Jia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9912)

Abstract

Training neural networks for semantic segmentation is data hungry. Meanwhile annotating a large number of pixel-level segmentation masks needs enormous human effort. In this paper, we propose a framework with only image-level supervision. It unifies semantic segmentation and object localization with important proposal aggregation and selection modules. They greatly reduce the notorious error accumulation problem that commonly arises in weakly supervised learning. Our proposed training algorithm progressively improves segmentation performance with augmented feedback in iterations. Our method achieves decent results on the PASCAL VOC 2012 segmentation data, outperforming previous image-level supervised methods by a large margin.

Keywords

Weakly supervised learning Semantic segmentation Image-level supervision Proposal aggregation 

Notes

Acknowledgments

This work is supported by a grant from the Research Grants Council of the Hong Kong SAR (project No. 2150760) and by the National Science Foundation China, under Grant 61133009. We thank the anonymous reviewers for their suggestive comments and valuable feedback, and Mr. Zhuotun Zhu for the helpful discussion regarding the topic of multiple instance learning.

References

  1. 1.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)Google Scholar
  2. 2.
    Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. arXiv (2014)Google Scholar
  3. 3.
    Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.H.: Conditional random fields as recurrent neural networks. In: ICCV (2015)Google Scholar
  4. 4.
    Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: CVPR (2015)Google Scholar
  5. 5.
    Dai, J., He, K., Sun, J.: Convolutional feature masking for joint object and stuff segmentation. In: CVPR (2015)Google Scholar
  6. 6.
    Dai, J., He, K., Sun, J.: Boxsup: exploiting bounding boxes to supervise convolutional networks for semantic segmentation. In: ICCV (2015)Google Scholar
  7. 7.
    Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. IJCV 88(2), 303–338 (2010)CrossRefGoogle Scholar
  8. 8.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: CVPR (2009)Google Scholar
  9. 9.
    Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 740–755. Springer, Heidelberg (2014)Google Scholar
  10. 10.
    Pinheiro, P.O., Collobert, R.: From image-level to pixel-level labeling with convolutional networks. In: CVPR (2015)Google Scholar
  11. 11.
    Pathak, D., Shelhamer, E., Long, J., Darrell, T.: Fully convolutional multi-class multiple instance learning. arXiv (2014)Google Scholar
  12. 12.
    Papandreou, G., Chen, L.C., Murphy, K., Yuille, A.L.: Weakly-and semi-supervised learning of a DCNN for semantic image segmentation. arXiv (2015)Google Scholar
  13. 13.
    Xu, J., Schwing, A.G., Urtasun, R.: Learning to segment under various forms of weak supervision. In: CVPR (2015)Google Scholar
  14. 14.
    Pathak, D., Krahenbuhl, P., Darrell, T.: Constrained convolutional neural networks for weakly supervised segmentation. In: ICCV (2015)Google Scholar
  15. 15.
    Song, H.O., Girshick, R., Jegelka, S., Mairal, J., Harchaoui, Z., Darrell, T.: On learning to localize objects with minimal supervision. arXiv (2014)Google Scholar
  16. 16.
    Kumar, M.P., Packer, B., Koller, D.: Self-paced learning for latent variable models. In: NIPS (2010)Google Scholar
  17. 17.
    Deselaers, T., Alexe, B., Ferrari, V.: Localizing objects while learning their appearance. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 452–466. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Simultaneous detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VII. LNCS, vol. 8695, pp. 297–312. Springer, Heidelberg (2014)Google Scholar
  19. 19.
    Liu, Z., Li, X., Luo, P., Loy, C.C., Tang, X.: Semantic image segmentation via deep parsing network. In: ICCV (2015)Google Scholar
  20. 20.
    Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: ICCV (2015)Google Scholar
  21. 21.
    Russakovsky, O., Bearman, A.L., Ferrari, V., Li, F.F.: What’s the point: semantic segmentation with point supervision. arXiv (2015)Google Scholar
  22. 22.
    Krähenbühl, P., Koltun, V.: Efficient inference in fully connected crfs with gaussian edge potentials. arXiv (2012)Google Scholar
  23. 23.
    Lin, D., Dai, J., Jia, J., He, K., Sun, J.: Scribblesup: scribble-supervised convolutional networks for semantic segmentation. In: CVPR (2016)Google Scholar
  24. 24.
    Vezhnevets, A., Buhmann, J.M.: Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning. In: CVPR (2010)Google Scholar
  25. 25.
    Wei, Y., Liang, X., Chen, Y., Shen, X., Cheng, M.M., Zhao, Y., Yan, S.: STC: a simple to complex framework for weakly-supervised semantic segmentation. arXiv (2015)Google Scholar
  26. 26.
    Wang, X., Zhu, Z., Yao, C., Bai, X.: Relaxed multiple-instance svm with application to object discovery. In: ICCV (2015)Google Scholar
  27. 27.
    Cinbis, R.G., Verbeek, J., Schmid, C.: Weakly supervised object localization with multi-fold multiple instance learning. arXiv (2015)Google Scholar
  28. 28.
    Song, H.O., Lee, Y.J., Jegelka, S., Darrell, T.: Weakly-supervised discovery of visual pattern configurations. In: NIPS (2014)Google Scholar
  29. 29.
    Uijlings, J.R., van de Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vis. (IJCV) 104(2), 154–171 (2013)CrossRefGoogle Scholar
  30. 30.
    Arbeláez, P., Pont-Tuset, J., Barron, J., Marques, F., Malik, J.: Multiscale combinatorial grouping. In: CVPR (2014)Google Scholar
  31. 31.
    Carreira, J., Sminchisescu, C.: Constrained parametric min-cuts for automatic object segmentation. In: CVPR (2010)Google Scholar
  32. 32.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv (2014)Google Scholar
  33. 33.
    Rother, C., Kolmogorov, V., Blake, A.: Grabcut: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. (TOG) 23(3), 309–314 (2004)CrossRefGoogle Scholar
  34. 34.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR (2014)Google Scholar
  35. 35.
    Hong, S., Noh, H., Han, B.: Decoupled deep neural network for semi-supervised semantic segmentation. In: NIPS (2015)Google Scholar
  36. 36.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Multimedia (2014)Google Scholar
  37. 37.
    Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv (2013)Google Scholar
  38. 38.
    Wei, Y., Liang, X., Chen, Y., Jie, Z., Xiao, Y., Zhao, Y., Yan, S.: Learning to segment with image-level annotations. PR (2016)Google Scholar
  39. 39.
    Hong, S., Oh, J., Han, B., Lee, H.: Learning transferrable knowledge for semantic segmentation with deep convolutional neural network. arXiv (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Xiaojuan Qi
    • 1
  • Zhengzhe Liu
    • 1
  • Jianping Shi
    • 2
  • Hengshuang Zhao
    • 1
  • Jiaya Jia
    • 1
  1. 1.The Chinese University of Hong KongShatinHong Kong
  2. 2.Sense Time Group LimitedBeijingChina

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