Fetching the paper…
Reading the bibliography…
The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data.
The unreasonable effectiveness of data
F. Pereira, P. Norvig, and A. Halev · 2009
Earlier work this paper cites.
Exploiting weakly-labeled web images to improve object classification: a domain adaptation approach
A. Bergamo and L. Torresani · 2010
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
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 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.
Unbiased look at dataset bias
A. Torralba and A. Efros · 2011
Earlier work this paper cites.
Large scale distributed deep networks
J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, Q. V. Le, M. Z. Mao, M. Ranzato, A. W. Senior, P. A. Tucker, K. Yang, and A. Y. Ng · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Neil: Extracting visual knowledge from web data
X. Chen, A. Shrivastava, and A. Gupta · 2013
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. B. Girshick, J. Donahue, T. Darrell, and J. Malik · 2013
Earlier work this paper cites.
Unsupervised joint object discovery and segmentation in internet images
M. Rubinstein, A. Joulin, J. Kopf, and C. Liu · 2013
Earlier work this paper cites.
Analyzing the performance of multilayer neural networks for object recognition
P. Agrawal, R. B. Girshick, and J. Malik · 2014
Earlier work this paper cites.
Learning everything about anything: Webly-supervised visual concept learning
S. Divvala, A. Farhadi, and C. Guestrin · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2014
Earlier work this paper cites.
Microsoft COCO: common objects in context
T. Lin, M. Maire, S. J. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Learning and transferring mid-level image representations using convolutional neural networks
M. Oquab, L. Bottou, I. Laptev, and J. Sivic · 2014
Cited alongside, same era.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. S. Bernstein, A. C. Berg, and F. Li · 2014
Cited alongside, same era.
Two-stream convolutional networks for action recognition in videos
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Webly supervised learning of convolutional networks
X. Chen and A. Gupta · 2015
Cited alongside, same era.
The new data and new challenges in multimedia research
B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L. Li · 2015
Later among the works it cites.
Unsupervised learning of visual representations using videos
X. Wang and A. Gupta · 2015
Later among the works it cites.
Realtime multi-person 2d pose estimation using part affinity fields
Z. Cao, T. Simon, S. Wei, and Y. Sheikh · 2016
Later among the works it cites.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2016
Later among the works it cites.
Xception: Deep learning with depthwise separable convolutions
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Deep classifiers from image tags in the wild
H. Izadinia, B. C. Russell, A. Farhadi, M. D. Hoffman, and A. Hertzmann · 2015
Cited alongside, same era.
What do 15,000 object categories tell us about classifying and localizing actions?
M. Jain, J. C. van Gemert, and C. G. Snoek · 2015
Cited alongside, same era.
Learning visual features from large weakly supervised data
A. Joulin, L. van der Maaten, A. Jabri, and N. Vasilache · 2015
Cited alongside, same era.
The unreasonable effectiveness of noisy data for fine-grained recognition
J. Krause, B. Sapp, A. Howard, H. Zhou, A. Toshev, T. Duerig, J. Philbin, and F. Li · 2015
Cited alongside, same era.
Large-scale deep learning on the YFCC100M dataset
K. Ni, R. A. Pearce, K. Boakye, B. V. Essen, D. Borth, B. Chen, and E. X. Wang · 2015
Cited alongside, same era.
F. Chollet · 2016
Later among the works it cites.
J. Donahue, P. Krähenbühl, and T. Darrell · 2016
Later among the works it cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Later among the works it cites.
What makes imagenet good for transfer learning?
M. Huh, P. Agrawal, and A. A. Efros · 2016
Later among the works it cites.
The curious robot: Learning visual representations via physical interactions
L. Pinto, D. Gandhi, Y. Han, Y. Park, and A. Gupta · 2016
Later among the works it cites.
Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, and V. Vanhoucke · 2016
Later among the works it cites.
Generating videos with scene dynamics
C. Vondrick, H. Pirsiavash, and A. Torralba · 2016
Later among the works it cites.
Planet - photo geolocation with convolutional neural networks
T. Weyand, I. Kostrikov, and J. Philbin · 2016
Later among the works it cites.
Speed/accuracy trade-offs for modern convolutional object detectors
J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, and K. Murphy · 2017
Closest in time.
Towards accurate multi-person pose estimation in the wild
G. Papandreou, T. Zhu, N. Kanazawa, A. Toshev, J. Tompson, C. Bregler, and K. Murphy · 2017
Closest in time.