]. P. Arbeláez, M. Maire, C. Fowlkes, and J. Malik, Contour Detection and Hierarchical Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.5, 2011.
DOI : 10.1109/TPAMI.2010.161

G. Bertasius, J. Shi, and L. Torresani, DeepEdge: A multi-scale bifurcated deep network for top-down contour detection, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7299067

URL : http://arxiv.org/abs/1412.1123

J. Canny, A computational approach to edge detection. TPAMI, pp.679-698, 1986.

J. K. Chorowski, D. Bahdanau, D. Serdyuk, K. Cho, and Y. Bengio, Attention-based models for speech recognition, NIPS, 2015.

X. Chu, W. Ouyang, and X. Wang, Crf-cnn: Modeling structured information in human pose estimation, NIPS, 2016.

J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412, 2014.

D. Comaniciu and P. Meer, Mean shift: a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.5, 2002.
DOI : 10.1109/34.1000236

J. Deng, W. Dong, R. Socher, L. Li, K. Li et al., Imagenet: A large-scale hierarchical image database, CVPR, 2009.

P. Dollár and C. L. Zitnick, Structured Forests for Fast Edge Detection, 2013 IEEE International Conference on Computer Vision, 2013.
DOI : 10.1109/ICCV.2013.231

P. Dollár and C. L. Zitnick, Fast Edge Detection Using Structured Forests, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.37, issue.8, pp.1558-1570, 2015.
DOI : 10.1109/TPAMI.2014.2377715

P. F. Felzenszwalb and D. P. Huttenlocher, Efficient Graph-Based Image Segmentation, International Journal of Computer Vision, vol.59, issue.2, 2004.
DOI : 10.1023/B:VISI.0000022288.19776.77

F. A. Gers, N. N. Schraudolph, and J. Schmidhuber, Learning precise timing with lstm recurrent networks, Journal of machine learning research, vol.3, pp.115-143, 2002.

S. Gupta, P. Arbelaez, and J. Malik, Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.79

URL : http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/papers/gam_cvpr2013.pdf

S. Gupta, R. Girshick, P. Arbeláez, and J. Malik, Learning Rich Features from RGB-D Images for Object Detection and Segmentation, ECCV, 2014.
DOI : 10.1007/978-3-319-10584-0_23

URL : http://www.cs.berkeley.edu/~sgupta/pdf/rcnn-depth-supp.pdf

S. Hallman and C. C. Fowlkes, Oriented edge forests for boundary detection, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298782

URL : http://www.ics.uci.edu/%7Efowlkes/papers/hf_oeforests_cvpr2015.pdf

B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik, Hypercolumns for object segmentation and finegrained localization, CVPR, 2015.
DOI : 10.1109/cvpr.2015.7298642

URL : http://www.cs.berkeley.edu/%7Ebharath2/pubs/pdfs/BharathCVPR2015.pdf

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2016.90

URL : http://arxiv.org/pdf/1512.03385

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long et al., Caffe, Proceedings of the ACM International Conference on Multimedia, MM '14, 2014.
DOI : 10.1145/2647868.2654889

I. Kokkinos, Pushing the boundaries of boundary detection using deep learning. arXiv preprint, 2015.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet classification with deep convolutional neural networks, Communications of the ACM, vol.60, issue.6, 2012.
DOI : 10.1162/neco.2009.10-08-881

URL : http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf

G. Li and Y. Yu, Visual saliency based on multiscale deep features, CVPR, 2015.

J. J. Lim, C. L. Zitnick, and P. Dollár, Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.406

URL : http://research.microsoft.com/en-us/um/people/larryz/CVPR13SketchTokens.pdf

Y. Liu, M. Cheng, X. Hu, K. Wang, and X. Bai, Richer Convolutional Features for Edge Detection, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2017.622

K. Maninis, J. Pont-tuset, P. Arbeláez, and L. Van-gool, Convolutional Oriented Boundaries, ECCV, 2016.
DOI : 10.1109/TPAMI.2015.2465908

URL : http://arxiv.org/pdf/1608.02755

D. R. Martin, C. C. Fowlkes, and J. Malik, Learning to detect natural image boundaries using local brightness, color, and texture cues, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.5, pp.530-549, 2004.
DOI : 10.1109/TPAMI.2004.1273918

V. Mnih, N. Heess, and A. Graves, Recurrent models of visual attention, NIPS, pp.2204-2212, 2014.

J. Pont-tuset, P. Arbelaez, J. Barron, F. Marques, and J. Malik, Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.39, issue.1, 2016.
DOI : 10.1109/TPAMI.2016.2537320

URL : http://arxiv.org/pdf/1503.00848

X. Ren, Multi-scale Improves Boundary Detection in Natural Images, ECCV, 2008.
DOI : 10.1109/ICCV.2007.4408985

Z. Ren and G. Shakhnarovich, Image Segmentation by Cascaded Region Agglomeration, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.262

W. Shen, X. Wang, Y. Wang, X. Bai, and Z. Zhang, Deepcontour: A deep convolutional feature learned by positive-sharing loss for contour detection, CVPR, 2015.

J. Shi and J. Malik, Normalized cuts and image segmentation, TPAMI, vol.22, issue.8, 2000.

N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, Indoor Segmentation and Support Inference from RGBD Images, ECCV, 2012.
DOI : 10.1007/978-3-642-33715-4_54

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint, 2014.

Y. Tang, Gated boltzmann machine for recognition under occlusion, NIPS Workshop on Transfer Learning by Learning Rich Generative Models, 2010.

J. Winn, Causality with gates, AISTATS, 2012.

T. Xiao, Y. Xu, K. Yang, J. Zhang, Y. Peng et al., The application of two-level attention models in deep convolutional neural network for fine-grained image classification, CVPR, 2015.

S. Xie and Z. Tu, Holistically-nested edge detection, ICCV, 2015.
DOI : 10.1007/s11263-017-1004-z

URL : http://arxiv.org/abs/1504.06375

D. Xu, E. Ricci, W. Ouyang, X. Wang, and N. Sebe, Multi-scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
DOI : 10.1109/CVPR.2017.25

K. Xu, J. Ba, R. Kiros, K. Cho, A. C. Courville et al., Show, attend and tell: Neural image caption generation with visual attention, ICML, 2015.

J. Yang, B. Price, S. Cohen, H. Lee, and M. Yang, Object Contour Detection with a Fully Convolutional Encoder-Decoder Network, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.28

S. Yang and D. Ramanan, Multi-scale Recognition with DAG-CNNs, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.144

URL : http://arxiv.org/pdf/1505.05232

F. Yu and V. Koltun, Multi-scale context aggregation by dilated convolutions. arXiv preprint, 2015.

X. Zeng, W. Ouyang, J. Yan, H. Li, T. Xiao et al., Crafting gbd-net for object detection. arXiv preprint, 2016.
DOI : 10.1109/tpami.2017.2745563

Z. Zhang, F. Xing, X. Shi, and L. Yang, SemiContour: A Semi-Supervised Learning Approach for Contour Detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.34

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5423734/pdf

Q. Zhao, Segmenting natural images with the least effort as humans, Procedings of the British Machine Vision Conference 2015, 2015.
DOI : 10.5244/C.29.110