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Semantic classes can be either things (objects with a well-defined shape, e.g.
Textures: A Photographic Album for Artists and Designers
P. Brodatz · 1966
Earlier work this paper cites.
Finding pictures of objects in large collections of images
D. Forsyth, J. Malik, M. Fleck, H. Greenspan, T. Leung, S. Belongie, and C. Bregler · 1996
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Reflectance and texture of real-world surfaces
K. Dana, B. Van Ginneken, S. Nayar, and J. Koenderink · 1999
Earlier work this paper cites.
On seeing stuff : The perception of materials by humans and machines
E. Adelson · 2001
Earlier work this paper cites.
Feature-rich part-of-speech tagging with a cyclic dependency network
K. Toutanova, D. Klein, C. Manning, and Y. Singer · 2003
Earlier work this paper cites.
Efficient graph-based image segmentation
P. F. Felzenszwalb and D. P. Huttenlocher · 2004
Earlier work this paper cites.
Grabcut: Interactive foreground extraction using iterated graph cuts
C. Rother, V. Kolmogorov, and A. Blake · 2004
Earlier work this paper cites.
A sparse texture representation using local affine regions
S. Lazebnik, C. Schmid, and J. Ponce · 2005
Earlier work this paper cites.
TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation
J. Shotton, J. Winn, C. Rother, and A. Criminisi · 2006
Earlier work this paper cites.
Objects in context
A. Rabinovich, A. Vedaldi, C. Galleguillos, E. Wiewiora, and S. Belongie · 2007
Earlier work this paper cites.
Learning spatial context: Using stuff to find things
G. Heitz and D. Koller · 2008
Earlier work this paper cites.
Semantic object classes in video: A high-definition ground truth database
G. J. Brostow, J. Fauqueur, and R. Cipolla · 2009
Earlier work this paper cites.
Decomposing a scene into geometric and semantically consistent regions
S. Gould, R. Fulton, and D. Koller · 2009
Earlier work this paper cites.
Robust higher order potentials for enforcing label consistency
P. Kohli, L. Ladicky, and P. Torr · 2009
Earlier work this paper cites.
Classifying materials in the real world
B. Caputo, E. Hayman, M. Fritz, and J.-O. Eklundh · 2010
Earlier work this paper cites.
Object detection with discriminatively trained part based models
P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan · 2010
Earlier work this paper cites.
Superparsing: Scalable nonparametric image parsing with superpixels
J. Tighe and S. Lazebnik · 2010
Earlier work this paper cites.
SUN database: Large-scale scene recognition from Abbey to Zoo
J. Xiao, J. Hays, K. Ehinger, A. Oliva, and A. Torralba · 2010
Earlier work this paper cites.
Probabilistic joint image segmentation and labeling
A. Ion, J. Carreira, and C. Sminchisescu · 2011
Earlier work this paper cites.
Efficient inference in fully connected CRFs with gaussian edge potentials
P. Krähenbühl and V. Koltun · 2011
Earlier work this paper cites.
Nonparametric scene parsing via label transfer
C. Liu, J. Yuen, and A. Torralba · 2011
Earlier work this paper cites.
SLIC superpixels compared to state-of-the-art superpixel methods
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk · 2012
Cited alongside, same era.
Video segmentation with superpixels
F. Galasso, R. Cipolla, and B. Schiele · 2012
Cited alongside, same era.
Are we ready for autonomous driving? the KITTI vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
Cited alongside, same era.
Relating things and stuff by high-order potential modeling
B. Kim, M. Sun, P. Kohli, and S. Savarese · 2012
Cited alongside, same era.
Indoor segmentation and support inference from rgbd images
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus · 2012
Cited alongside, same era.
Parsing clothing in fashion photographs
K. Yamaguchi, M. H. Kiapour, L. E. Ortiz, and T. L. Berg · 2012
Cited alongside, same era.
Semantic image segmentation with deep convolutional nets and fully connected CRFs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2015
Later among the works it cites.
Convolutional feature masking for joint object and stuff segmentation
J. Dai, K. He, and J. Sun · 2015
Later among the works it cites.
Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
D. Eigen and R. Fergus · 2015
Later among the works it cites.
The PASCAL visual object classes challenge: A retrospective
M. Everingham, S. Eslami, L. van Gool, C. Williams, J. Winn, and A. Zisserman · 2015
Later among the works it cites.
Training deep networks with structured layers by matrix backpropagation
C. Ionescu, O. Vantzos, and C. Sminchisescu · 2015
Later among the works it cites.
Fully convolutional networks for semantic segmentation
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OpenSurfaces: A richly annotated catalog of surface appearance
S. Bell, P. Upchurch, N. Snavely, and K. Bala · 2013
Cited alongside, same era.
Analyzing semantic segmentation using hybrid human-machine CRFs
R. Mottaghi, S. Fidler, J. Yao, R. Urtasun, and D. Parikh · 2013
Cited alongside, same era.
Finding things: Image parsing with regions and per-exemplar detectors
J. Tighe and S. Lazebnik · 2013
Cited alongside, same era.
Superparsing - scalable nonparametric image parsing with superpixels
J. Tighe and S. Lazebnik · 2013
Cited alongside, same era.
Selective search for object recognition
J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders · 2013
Cited alongside, same era.
Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi · 2014
Cited alongside, same era.
J. Long, E. Shelhamer, and T. Darrell · 2015
Later among the works it cites.
Semi-automatic video object segmentation by advanced manipulation of segmentation hierarchies
J. Pont-Tuset, M. A. F. Guiu, and A. Smolic · 2015
Later among the works it cites.
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. Berg, and L. Fei-Fei · 2015
Later among the works it cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Later among the works it cites.
Semantic part segmentation using compositional model combining shape and appearance
J. Wang and A. Yuille · 2015
Later among the works it cites.
Learning to segment under various forms of weak supervision
J. Xu, A. G. Schwing, and R. Urtasun · 2015
Later among the works it cites.
Sensor fusion for semantic segmentation of urban scenes
R. Zhang, S. A. Candra, K. Vetter, and A. Zakhor · 2015
Later among the works it cites.
What’s the point: Semantic segmentation with point supervision
A. Bearman, O. Russakovsky, V. Ferrari, and L. Fei-Fei · 2016
Closest in time.
Region-based semantic segmentation with end-to-end training
H. Caesar, J. Uijlings, and V. Ferrari · 2016
Closest in time.
The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Closest in time.
Click carving: Segmenting objects in video with point clicks
S. Jain and K. Grauman · 2016
Closest in time.
Annotating object instances with a Polygon-RNN
L. Castrejon, K. Kundu, R. Urtasun, and S. Fidler · 2017
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Weakly supervised object localization using things and stuff transfer
M. Shi, H. Caesar, and V. Ferrari · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
C. Sun, A. Shrivastava, S. Singh, and A. Gupta · 2017
Closest in time.
Scene parsing through ADE20K dataset
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba · 2017
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