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The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects.
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Do deep nets really need to be deep?
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Deep residual learning for image recognition
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Multi-class multi-object tracking using changing point detection
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A multipath network for object detection
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Real time image saliency for black box classifiers
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Understanding the effective receptive field in deep convolutional neural networks
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Attention is all you need
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Aggregated residual transformations for deep neural networks
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Deformable convolutional networks
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei · 2017
Cited alongside, same era.
M. Denil, S. G. Colmenarejo, S. Cabi, D. Saxton, and N. de Freitas · 2017
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Interpretable explanations of black boxes by meaningful perturbation
R. C. Fong and A. Vedaldi · 2017
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A convolutional encoder model for neural machine translation
J. Gehring, M. Auli, D. Grangier, and Y. N. Dauphin · 2017
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Convolutional sequence to sequence learning
J. Gehring, M. Auli, D. Grangier, D. Yarats, and Y. N. Dauphin · 2017
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Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Cited alongside, same era.
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
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Flow-guided feature aggregation for video object detection
X. Zhu, Y. Wang, J. Dai, L. Yuan, and Y. Wei · 2017
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Deep feature flow for video recognition
X. Zhu, Y. Xiong, J. Dai, L. Yuan, and Y. Wei · 2017
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Visualizing deep neural network decisions: Prediction difference analysis
L. M. Zintgraf, T. S. Cohen, T. Adel, and M. Welling · 2017
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Revisiting rcnn: On awakening the classification power of faster rcnn
B. Cheng, Y. Wei, H. Shi, R. Feris, J. Xiong, and T. Huang · 2018
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Detectron
R. Girshick, I. Radosavovic, G. Gkioxari, P. Dollár, and K. He · 2018
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Learning region features for object detection
J. Gu, H. Hu, L. Wang, Y. Wei, and J. Dai · 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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Non-local neural networks
X. Wang, R. Girshick, A. Gupta, and K. He · 2018
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