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We explore design principles 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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Natural Image Statistics: A Probabilistic Approach to Early Computational Vision
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The PASCAL Visual Object Classes (VOC) Challenge
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Object detection with discriminatively trained part-based models
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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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Semantic contours from inverse detectors
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Efficient inference in fully connected crfs with gaussian edge potentials
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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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Efficient backprop
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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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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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Automated target segmentation and real space fast alignment methods for high-throughput classification and averaging of crowded cryo-electron subtomograms
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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Learning image representations tied to ego-motion
D. Jayaraman and K. Grauman · 2015
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Parsenet: Looking wider to see better
W. Liu, A. Rabinovich, and A. C. Berg · 2015
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M. Xu and F. Alber · 2013
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Analyzing the performance of multilayer neural networks for object recognition
P. Agrawal, R. Girshick, and J. Malik · 2014
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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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Unfolding an indoor origami world
D. F. Fouhey, A. Gupta, and M. Hebert · 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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Crisp boundary detection using pointwise mutual information
P. Isola, D. Zoran, D. Krishnan, and E. H. Adelson · 2014
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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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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ImageNet large scale visual recognition challenge
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 · 2015
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Designing deep networks for surface normal estimation
X. Wang, D. Fouhey, and A. Gupta · 2015
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Unsupervised learning of visual representations using videos
X. Wang 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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Realtime multi-person 2d pose estimation using part affinity fields
Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh · 2016
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L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2016
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J. Donahue, P. Krähenbühl, and T. Darrell · 2016
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Unsupervised CNN for single view depth estimation: Geometry to the rescue
R. Garg, B. G. V. Kumar, G. Carneiro, and I. D. Reid · 2016
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I. Kokkinos · 2016
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Learning representations for automatic colorization
G. Larsson, M. Maire, and G. Shakhnarovich · 2016
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Unsupervised visual representation learning by graph-based consistent constraints
D. Li, W.-C. Hung, J.-B. Huang, S. Wang, N. Ahuja, and M.-H. Yang · 2016
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Shuffle and Learn: Unsupervised Learning using Temporal Order Verification
I. Misra, C. L. Zitnick, and M. Hebert · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
M. Noroozi and P. Favaro · 2016
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Ambient sound provides supervision for visual learning
A. Owens, J. Wu, J. H. McDermott, W. T. Freeman, and A. Torralba · 2016
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Context encoders: Feature learning by inpainting
D. Pathak, P. Krähenbühl, J. Donahue, T. Darrell, and A. Efros · 2016
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The curious robot: Learning visual representations via physical interactions
L. Pinto, D. Gandhi, Y. Han, Y. Park, and A. Gupta · 2016
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Learning to extract motion from videos in convolutional neural networks
D. Teney and M. Hebert · 2016
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Convolutional pose machines
S.-E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh · 2016
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Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
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Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
R. Zhang, P. Isola, and A. A. Efros · 2016
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An implementation of faster rcnn with study for region sampling
X. Chen and A. Gupta · 2017
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