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We provide a detailed analysis of convolutional neural networks which are pre-trained on the task of object detection.
Caltech-256 object category dataset
G. Griffin, A. Holub, and P. Perona · 2007
Earlier work this paper cites.
Visualizing data using t-sne
L. v. d. Maaten and G. Hinton · 2008
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Automated flower classification over a large number of classes
M.-E. Nilsback and A. Zisserman · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Visualizing higher-layer features of a deep network
D. Erhan, Y. Bengio, A. Courville, and P. Vincent · 2009
Earlier work this paper cites.
Robust higher order potentials for enforcing label consistency
P. Kohli, P. H. Torr, et al · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
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Sun database: Large-scale scene recognition from abbey to zoo
J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba · 2010
Earlier work this paper cites.
Semantic contours from inverse detectors
B. Hariharan, P. Arbeláez, L. Bourdev, S. Maji, and J. Malik · 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.
Diagnosing error in object detectors
D. Hoiem, Y. Chodpathumwan, and Q. Dai · 2012
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
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L. Van Der Maaten · 2013
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Semantic image segmentation with deep convolutional nets and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2014
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Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 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
Earlier work this paper cites.
Simultaneous detection and segmentation
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
Cnn features off-the-shelf: an astounding baseline for recognition
A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson · 2014
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Understanding neural networks through deep visualization
J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson · 2015
Cited alongside, same era.
Factors of transferability for a generic convnet representation
H. Azizpour, A. S. Razavian, J. Sullivan, A. Maki, and S. Carlsson · 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
Pyramid scene parsing network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2018
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Encoder-decoder with atrous separable convolution for semantic image segmentation
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam · 2018
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Large scale fine-grained categorization and domain-specific transfer learning
Y. Cui, Y. Song, C. Sun, A. Howard, and S. Belongie · 2018
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Rethinking imagenet pre-training
K. He, R. Girshick, and P. Dollár · 2018
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Cited alongside, same era.
Scene recognition with cnns: objects, scales and dataset bias
L. Herranz, S. Jiang, and X. Li · 2016
Cited alongside, same era.
What makes imagenet good for transfer learning?
M. Huh, P. Agrawal, and A. A. Efros · 2016
Cited alongside, same era.
Deep neural networks as a computational model for human shape sensitivity
J. Kubilius, S. Bracci, and H. P. O. de Beeck · 2016
Cited alongside, same era.
Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
Cited alongside, same era.
Visualizing deep convolutional neural networks using natural pre-images
A. Mahendran and A. Vedaldi · 2016
Cited alongside, same era.
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
A. Nguyen, A. Dosovitskiy, J. Yosinski, T. Brox, and J. Clune · 2016
Cited alongside, same era.
S. Kornblith, J. Shlens, and Q. V. Le · 2018
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A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci, T. Duerig, and V. Ferrari · 2018
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Cornernet: Detecting objects as paired keypoints
H. Law and J. Deng · 2018
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Exploring the limits of weakly supervised pretraining
D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten · 2018
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An analysis of scale invariance in object detection snip
B. Singh and L. S. Davis · 2018
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R-fcn-3000 at 30fps: Decoupling detection and classification
B. Singh, H. Li, A. Sharma, and L. S. Davis · 2018
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Sniper: Efficient multi-scale training
B. Singh, M. Najibi, and L. S. Davis · 2018
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Non-local neural networks
X. Wang, R. Girshick, A. Gupta, and K. He · 2018
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Videos as space-time region graphs
X. Wang and A. Gupta · 2018
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Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification
S. Xie, C. Sun, J. Huang, Z. Tu, and K. Murphy · 2018
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Taskonomy: Disentangling task transfer learning
A. R. Zamir, A. Sax, W. Shen, L. J. Guibas, J. Malik, and S. Savarese · 2018
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Temporal relational reasoning in videos
B. Zhou, A. Andonian, A. Oliva, and A. Torralba · 2018
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Places: A 10 million image database for scene recognition
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba · 2018
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Deformable convnets v2: More deformable, better results
X. Zhu, H. Hu, S. Lin, and J. Dai · 2018
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Approximating cnns with bag-of-local-features models works surprisingly well on imagenet
W. Brendel and M. Bethge · 2019
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Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel · 2019
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