ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn , Issue , 2016, Pages 426-433

  Visin, Francesco ^a,b   Romero, Adriana ^b   Cho, Kyunghyun ^c   Matteucci, Matteo ^a   Ciccone, Marco ^a   Kastner, Kyle ^b   Bengio, Yoshua ^d   Courville, Aaron ^b  

^a POLITECNICO DI MILANO   (Italy)

^b UNIVERSITÉ DE MONTRÉAL   (Canada)

^c NEW YORK UNIVERSITY   (United States)

^d CIFAR   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CONVOLUTION; FORECASTING; IMAGE RESOLUTION; NETWORK ARCHITECTURE; NEURAL NETWORKS; PATTERN RECOGNITION; SEMANTIC WEB; SEMANTICS;

CONVOLUTIONAL NEURAL NETWORK; GENERIC FEATURES; GLOBAL INFORMATIONS; PROPOSED ARCHITECTURES; RECURRENT NEURAL NETWORK (RNN); SEMANTIC SEGMENTATION; STATE-OF-THE-ART PERFORMANCE; STRUCTURED PREDICTION;

RECURRENT NEURAL NETWORKS;

EID: 85010223513     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2016.60     Document Type: Conference Paper

References (50)

1. V. Badrinarayanan, A. Handa, and R. Cipolla. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling
2. V. Badrinarayanan, A. Handa, and R. Cipolla. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. page 5, 2015
3. F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. Goodfellow, A. Bergeron, N. Bouchard, D. Warde-Farley, and Y. Bengio. Theano: new features and speed improvements. Submited to the Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop, 2012
4. J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, and Y. Bengio. Theano: A CPU and GPU math expression compiler. In Proceedings of the Python for Scientific Computing Conference (SciPy), 2010
5. L. Bertelli, T. Yu, D. Vu, and B. Gokturk. Kernelized structural svm learning for supervised object segmentation. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 2153-2160. IEEE, 2011
6. E. Borenstein. Combining top-down and bottom-up segmentation. In In Proceedings IEEE workshop on Perceptual Organization in Computer Vision, CVPR, page 46, 2004
7. G. J. Brostow, J. Fauqueur, and R. Cipolla. Semantic object classes in video: A high-definition ground truth database. Pattern Recognition Letters, 30(2):88-97, 2009
8. W. Byeon, T. M. Breuel, F. Raue, and M. Liwicki. Scene labeling with lstm recurrent neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3547-3555, 2015
9. L.-C. Chen, J. T. Barron, G. Papandreou, K. Murphy, and A. L. Yuille. Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform. ArXiv preprint arXiv:1511.03328, 2015
10. K. Cho, B. van Merrienboer, C. Gulcehre, F. Bougares, H. Schwenk, and Y. Bengio. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the Empiricial Methods in Natural Language Processing (EMNLP 2014), Oct. 2014. to appear
11. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR09, 2009
12. S. Dieleman, J. Schlüter, C. Raffel, E. Olson, S. K. Sønderby, D. Nouri, D. Maturana, M. Thoma, E. Battenberg, J. Kelly, J. D. Fauw, M. Heilman, diogo149, B. McFee, H. Weideman, takacsg84, peterderivaz, Jon, instagibbs, D. K. Rasul, CongLiu, Britefury, and J. Degrave. Lasagne: First release., Aug. 2015
13. V. Dumoulin and F. Visin. A guide to convolution arithmetic for deep learning, 2016. cite arxiv:1603.07285
14. D. Eigen and R. Fergus. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. CoRR, abs/1411.4734, 2014
15. D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov. Scalable object detection using deep neural networks. In Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR '14, pages 2155-2162,Washington, DC, USA, 2014. IEEE Computer Society
16. C. Farabet, C. Couprie, L. Najman, and Y. LeCun. Learning hierarchical features for scene labeling. IEEE TPAMI, 35(8):1915-1929, 2013
17. C. Gatta, A. Romero, and J. van de Weijer. Unrolling loopy top-down semantic feedback in convolutional deep networks. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR Workshops 2014, Columbus, OH, USA, June 23-28, 2014, pages 504-511, 2014
18. X. Glorot and Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. In International conference on artificial intelligence and statistics, pages 249-256, 2010
19. B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik. Hypercolumns for object segmentation and fine-grained localization. In Computer Vision and Pattern Recognition (CVPR), 2015
20. S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Computation, 9(8):1735-1780, 1997
21. D. J. Im, C. D. Kim, H. Jiang, and R. Memisevic. Generating images with recurrent adversarial networks. ArXiv preprint arXiv:1602.05110, 2016
22. A. Kendall, V. Badrinarayanan, and R. Cipolla. Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding. 2015
23. A. Krizhevsky, I. Sutskever, and G. Hinton. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25 (NIPS'2012). 2012
24. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural. In F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 25, pages 1097-1105. Curran Associates, Inc., 2012
25. A. Krogh and J. A. Hertz. A simple weight decay can improve generalization. In ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 4, pages 950-957. Morgan Kaufmann, 1992
26. L. Ladicky, P. Sturgess, K. Alahari, C. Russell, and P. H. S. Torr. What, where and how many Combining object detectors and CRFs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6314 LNCS(PART 4):424-437, 2010
27. C. Laurent, G. Pereyra, P. Brakel, Y. Zhang, and Y. Bengio. Batch normalized recurrent neural networks. CoRR, abs/1510.01378, 2015
28. M. Lin, Q. Chen, and S. Yan. Network in network. In Proceedings of the Second International Conference on Learning Representations (ICLR 2014), Apr. 2014
29. F. Liu, G. Lin, and C. Shen. Crf learning with cnn features for image segmentation. Pattern Recognition, 2015
30. J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. CVPR (to appear), Nov. 2015
31. J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3431-3440, 2015
32. M.-E. Nilsback and A. Zisserman. A visual vocabulary for flower classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 1447-1454, 2006
33. H. Noh, S. Hong, and B. Han. Learning deconvolution network for semantic segmentation. ArXiv preprint arXiv:1505.04366, 2015
34. P. Pinheiro and R. Collobert. Recurrent convolutional neural networks for scene labeling. JMLR, 1(32):82-90, 2014
35. A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. ArXiv preprint arXiv:1511.06434, 2015
36. O. Ronneberger, P.Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 9351 of LNCS, pages 234-241. Springer, 2015. (available on arXiv:1505.04597 [cs.CV])
37. C. Rother, V. Kolmogorov, and A. Blake. Grabcut: Interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics (TOG), 23(3):309-314, 2004
38. A. M. Saxe, J. L. McClelland, and S. Ganguli. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. In Proceedings of the Second International Conference on Learning Representations (ICLR 2014), Apr. 2014
39. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015
40. G. Singh and J. Kosecka. Nonparametric scene parsing with adaptive feature relevance and semantic context. In 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, June 23-28, 2013, pages 3151-3157, 2013
41. M. F. Stollenga, W. Byeon, M. Liwicki, and J. Schmidhuber. Parallel multi-dimensional lstm, with application to fast biomedical volumetric image segmentation. In Advances in Neural Information Processing Systems, pages 2980-2988, 2015
42. P. Sturgess, K. Alahari, L. Ladicky, and P. H. S. Torr. Combining Appearance and Structure from Motion Features for Road Scene Understanding. Procedings of the British Machine Vision Conference 2009, pages 62.1-62.11, 2009
43. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. ArXiv preprint arXiv:1409.4842, 2014
44. J. Tighe and S. Lazebnik. Superparsing: Scalable nonparametric image parsing with superpixels. International Journal of Computer Vision, 101(2):329-349, 2013
45. F. Visin, K. Kastner, K. Cho, M. Matteucci, A. Courville, and Y. Bengio. Renet: A recurrent neural network based alternative to convolutional networks. ArXiv preprint arXiv:1505.00393, 2015
46. X. Wu and K. Kashino. Tri-map self-validation based on least gibbs energy for foreground segmentation. In Proceedings of the British Machine Vision Conference. BMVA Press, 2014
47. J. Yang, B. Price, S. Cohen, Z. Lin, and M.-H. Yang. Patchcut: Data-driven object segmentation via local shape transfer. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1770-1778, 2015
48. M. Zeiler, G. Taylor, and R. Fergus. Adaptive deconvolutional networks for mid and high level feature learning. In Proc. International Conference on Computer Vision (ICCV'11), pages 2146-2153. IEEE, 2011
49. M. D. Zeiler. ADADELTA: An adaptive learning rate method. Technical report, arXiv 1212.5701, 2012
50. M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In ECCV'14, 2014.