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The dominant object detection approaches treat the recognition of each region separately and overlook crucial semantic correlations between objects in one scene.
Scene perception: Detecting and judging objects undergoing relational violations
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Sharing features: efficient boosting procedures for multiclass object detection
A. Torralba, K. P. Murphy, and W. T. Freeman · 2004
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Object categorization using co-occurrence, location and appearance
C. Galleguillos, A. Rabinovich, and S. Belongie · 2008
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Describing objects by their attributes
A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth · 2009
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Region-based segmentation and object detection
S. Gould, T. Gao, and D. Koller · 2009
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Learning to detect unseen object classes by between-class attribute transfer
C. H. Lampert, H. Nickisch, and S. Harmeling · 2009
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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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Relative attributes
D. Parikh and K. Grauman · 2011
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Learning to share visual appearance for multiclass object detection
R. Salakhutdinov, A. Torralba, and J. Tenenbaum · 2011
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Metric learning for large scale image classification: Generalizing to new classes at near-zero cost
T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka · 2012
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Label-embedding for attribute-based classification
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Devise: A deep visual-semantic embedding model
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Transfer learning in a transductive setting
M. Rohrbach, S. Ebert, and B. Schiele · 2013
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Word spotting and recognition with embedded attributes
J. Almazán, A. Gordo, A. Fornés, and E. Valveny · 2014
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Large-scale object classification using label relation graphs
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Lsda: Large scale detection through adaptation
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Zero-shot recognition with unreliable attributes
D. Jayaraman and K. Grauman · 2014
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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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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
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Learning like a child: Fast novel visual concept learning from sentence descriptions of images
J. Mao, X. Wei, Y. Yang, J. Wang, Z. Huang, and A. L. Yuille · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Detecting visual relationships with deep relational networks
B. Dai, Y. Zhang, and D. Lin · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Attentive contexts for object detection
J. Li, Y. Wei, X. Liang, J. Dong, T. Xu, J. Feng, and S. Yan · 2017
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Light-head r-cnn: In defense of two-stage object detector
Z. Li, C. Peng, G. Yu, X. Zhang, Y. Deng, and J. Sun · 2017
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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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The more you know: Using knowledge graphs for image classification
K. Marino, R. Salakhutdinov, and A. Gupta · 2017
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S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Imagenet large scale visual recognition challenge
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2015
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R-fcn: Object detection via region-based fully convolutional networks
J. Dai, Y. Li, K. He, and J. Sun · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L.-J. Li, D. A. Shamma, M. Bernstein, and L. Fei-Fei · 2016
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From red wine to red tomato: Composition with context
I. Misra, A. Gupta, and M. Hebert · 2017
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Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
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A faster pytorch implementation of faster r-cnn
J. Yang, J. Lu, D. Batra, and D. Parikh · 2017
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Scene parsing through ade20k dataset
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba · 2017
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Cascade r-cnn: Delving into high quality object detection
Z. Cai and N. Vasconcelos · 2018
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Iterative visual reasoning beyond convolutions
X. Chen, L.-J. Li, L. Fei-Fei, and A. Gupta · 2018
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Few-shot learning with graph neural networks
V. Garcia and J. Bruna · 2018
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Relation networks for object detection
H. Hu, J. Gu, Z. Zhang, J. Dai, and Y. Wei · 2018
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Learning to segment every thing
R. Hu, P. Dollár, K. He, T. Darrell, and R. Girshick · 2018
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Non-local neural networks
X. Wang, R. Girshick, A. Gupta, and K. He · 2018
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Zero-shot recognition via semantic embeddings and knowledge graphs
X. Wang, Y. Ye, and A. Gupta · 2018
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