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The basic principles in designing convolutional neural network (CNN) structures for predicting objects on different levels, e.g., image-level, region-level, and pixel-level are diverging.
Imagenet classification with deep convolutional neural networks
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Microsoft coco: Common objects in context
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Very deep convolutional networks for large-scale image recognition
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Hypercolumns for object segmentation and fine-grained localization
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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U-net: Convolutional networks for biomedical image segmentation
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Imagenet large scale visual recognition challenge
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Going deeper with convolutions
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Conditional random fields as recurrent neural networks
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Crf-cnn: Modeling structured information in human pose estimation
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Fractalnet: Ultra-deep neural networks without residuals
G. Larsson, M. Maire, and G. Shakhnarovich · 2016
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Stacked hourglass networks for human pose estimation
A. Newell, K. Yang, and J. Deng · 2016
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S. Zagoruyko and N. Komodakis · 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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Dual path networks
Y. Chen, J. Li, H. Xiao, X. Jin, S. Yan, and J. Feng · 2017
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Accurate, large minibatch sgd: training imagenet in 1 hour
P. Goyal, P. Dollár, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He · 2017
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Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Learning feature pyramids for human pose estimation
W. Yang, S. Li, W. Ouyang, H. Li, and X. Wang · 2017
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mmdetection
K. Chen, J. Pang, J. Wang, Y. Xiong, X. Li, S. Sun, W. Feng, Z. Liu, J. Shi, W. Ouyang, C. C. Loy, and D. Lin · 2018
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Question-guided hybrid convolution for visual question answering
P. Gao, H. Li, S. Li, P. Lu, Y. Li, S. C. Hoi, and X. Wang · 2018
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Detectron
R. Girshick, I. Radosavovic, G. Gkioxari, P. Dollár, and K. He · 2018
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i-revnet: Deep invertible networks
J.-H. Jacobsen, A. Smeulders, and E. Oyallon · 2018
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Squeeze-and-excitation networks
J. Hu, L. Shen, and G. 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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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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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun
Cited in the paper.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun
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Nestednet: Learning nested sparse structures in deep neural networks
E. Kim, C. Ahn, and S. Oh · 2018
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Zoom out-and-in network with map attention decision for region proposal and object detection
H. Li, Y. Liu, W. Ouyang, and X. Wang · 2018
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Optical flow guided feature: A fast and robust motion representation for video action recognition
S. Sun, Z. Kuang, L. Sheng, W. Ouyang, and W. Zhang · 2018
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Convolutional neural networks with alternately updated clique
Y. Yang, Z. Zhong, T. Shen, and Z. Lin · 2018
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Deep continuous conditional random fields with asymmetric inter-object constraints for online multi-object tracking
H. Zhou, W. Ouyang, J. Cheng, X. Wang, and H. Li · 2018
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