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Context is important for accurate visual recognition.
Context-based vision system for place and object recognition
A. Torralba, K. P. Murphy, W. T. Freeman, and M. A. Rubin · 2003
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Textonboost: Joint appearance,shape and context modeling for multi-class object recognition and segmentation
J. Shotton, J. Winn, C. Rother, and A. Criminisi · 2006
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Objects in context
A. Rabinovich, A. Vedaldi, C. Galleguillos, E. Wiewiora, and S. Belongie · 2007
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Object categorization using co-occurrence, location and appearance
C. Galleguillos, A. Rabinovich, and S. Belongie · 2008
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Learning spatial context: Using stuff to find things
G. Heitz and D. Koller · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei · 2009
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An empirical study of context in object detection
S. K. Divvala, D. Hoiem, J. H. Hays, A. A. Efros, and M. Hebert · 2009
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Exloiting hierachical context on a large database of object categories
M. J. Choi, J. Lim, A. Torralba, and A. S. Willsky · 2010
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The pascal visual object classes (voc) challenge
M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
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Using the forest to see the trees: object recognition in context
A. Torralba, K. Murphy, and W. T. Freeman · 2010
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Searching for objects driven by context
B. Alexe, N. Heess, Y. W. Teh, and V. Ferrari · 2012
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Supervised Sequence Labelling with Recurrent Neural Networks
A. Graves · 2012
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Diagnosing error in object detectors
D. Hoiem, Y. Chodpathumwan, and Q. Dai · 2012
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On the properties of neural machine translation: Encoder-decoder approaches
K. Cho, B. van Merrienboer, D. Bahdanau, and Y. Bengio · 2014
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Object detection with discriminatively trained part based models
P. F. Felzenszwalb, R. B.Girshick, D. McAllester, and D. Ramanan · 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
Cited alongside, same era.
Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, and P. Dollar · 2014
Cited alongside, same era.
The role of context for object detection and semantic segmentation in the wild
R. Mottaghi, X. Chen, X. Liu, N.-G. Cho, S.-W. Lee, S. Fidler, R. Urtasun, and A. Yuille · 2014
Cited alongside, same era.
Fast r-cnn
R. Girshick · 2015
Cited alongside, same era.
Exploring person context and local scene context for object detection
S. Gupta, B. Hariharan, and J. Malik · 2015
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
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You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
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Contextual priming and feedback for faster r-cnn
A. Shrivastava and A. Gupta · 2016
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Hypernet: Towards accurate region proposal generation and joint object detection
K. Tao, A. Yao, C. Yurong, and S. Fuchun · 2016
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Gated bi-directional cnn for object detection
X. Zeng, W. Ouyang, B. Yang, J. Yan, and X. Wang · 2016
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Spatial memory for context reasoning in object detection
X. Chen and A. Gupta · 2017
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S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
Interaction networks for learning about objects, relations and physics
P. W. Battaglia, R. Pascanu, and M. Lai · 2016
Cited alongside, same era.
Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks
S. Bell, C. L. Zitnick, K. Bala, and R. Girshick · 2016
Cited alongside, same era.
R-fcn: Object detection via region-based fully convolutional networks
J. Dai, Y. Li, K. He, and J. Sun · 2016
Cited alongside, same era.
Structure inference machines: Recurrent neural networks for analyzing relations in group activity recognition
Z. Deng, A. Vahdat, H. Hu, and G. Mori · 2016
Cited alongside, same era.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems, 2016
M. A. et al · 2016
Cited alongside, same era.
Learning structured inference neural networks with label relations
H. Hu, G.-T. Zhou, Z. Deng, Z. Liao, and G. Mori · 2016
Cited alongside, same era.
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Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
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Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
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The more you know: Using knowledge graphs for image classification
K. Marino, R. Salakhutdinov, and A. Gupta · 2017
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Yolo9000: Better, faster, stronger
J. Redmon and A. Farhadi · 2017
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Structured sequence modeling with graph convolutional recurrent networks
Y. Seo, M. Defferrard, P. Vandergheynst, and X. Bresson · 2017
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Dsod: Learning deeply supervised object detectors from scratch
Z. Shen, Z. Liu, J. Li, Y.-G. Jiang, Y. Chen, and X. Xue · 2017
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Graph-structured representations for visual question answering
D. Teney, L. Liu, and A. van den Hengel · 2017
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Scene graph generation by iterative message passing
D. Xu, Y. Zhu, C. B. Choy, and L. Fei-Fei · 2017
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