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We explore architectures for general pixel-level prediction problems, from low-level edge detection to mid-level surface normal estimation to high-level semantic segmentation.
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Auto-context and its application to high-level vision tasks and 3d brain image segmentation
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Contour detection and hierarchical image segmentation
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Nonparametric scene parsing via label transfer
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Semantic segmentation using regions and parts
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Semantic segmentation with second-order pooling
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Large scale distributed deep networks
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Indoor segmentation and support inference from rgbd images
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus · 2012
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Discriminatively trained sparse code gradients for contour detection
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Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation
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Structured forests for fast edge detection
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Deep generative image models using a laplacian pyramid of adversarial networks
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Fast edge detection using structured forests
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
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Flownet: Learning optical flow with convolutional networks
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Learning hierarchical features for scene labeling
C. Farabet, C. Couprie, L. Najman, and Y. LeCun · 2013
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Data-driven 3D primitives for single image understanding
D. F. Fouhey, A. Gupta, and M. Hebert · 2013
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Sketch tokens: A learned mid-level representation for contour and object detection
J. Lim, C. Zitnick, and P. Dollár · 2013
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Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Using k-poselets for detecting people and localizing their keypoints
G. Gkioxari, B. Hariharan, R. Girshick, and J. Malik · 2014
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Fast r-cnn
R. Girshick · 2015
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Oriented edge forests for boundary detection
S. Hallman and C. C. Fowlkes · 2015
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Hypercolumns for object segmentation and fine-grained localization
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Densebox: Unifying landmark localization with end to end object detection
L. Huang, Y. Yang, Y. Deng, and Y. Yu · 2015
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Pixel-wise deep learning for contour detection
J.-J. Hwang and T.-L. Liu · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Parsenet: Looking wider to see better
W. Liu, A. Rabinovich, and A. C. Berg · 2015
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Fully convolutional models for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Feedforward semantic segmentation with zoom-out features
M. Mostajabi, P. Yadollahpour, and G. Shakhnarovich · 2015
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Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
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Visual tracking with fully convolutional networks
L. Wang, W. Ouyang, X. Wang, and H. Lu · 2015
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Designing deep networks for surface normal estimation
X. Wang, D. Fouhey, and A. Gupta · 2015
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Convolutional pseudo-prior for structured labeling
S. Xie, X. Huang, and Z. Tu · 2015
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Holistically-nested edge detection
S. Xie and Z. Tu · 2015
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Conditional random fields as recurrent neural networks
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. H. Torr · 2015
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Marr Revisited: 2D-3D model alignment via surface normal prediction
A. Bansal, B. Russell, and A. Gupta · 2016
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L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2016
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Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
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