Fetching the paper…
Reading the bibliography…
The non-local module is designed for capturing long-range spatio-temporal dependencies in images and videos.
Networks for approximation and learning
T. Poggio and F. Girosi · 1990
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Learning with kernels: support vector machines, regularization, optimization, and beyond
B. Scholkopf and A. J. Smola · 2001
Earlier work this paper cites.
Distinctive image features from scale-invariant keypoints
D. G. Lowe · 2004
Earlier work this paper cites.
Improving the fisher kernel for large-scale image classification
F. Perronnin, J. Sánchez, and T. Mensink · 2010
Earlier work this paper cites.
The Caltech-UCSD birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
Earlier work this paper cites.
Action recognition by dense trajectories
H. Wang, A. Kläser, C. Schmid, and C.-L. Liu · 2011
Earlier work this paper cites.
Semantic segmentation with second-order pooling
J. Carreira, R. Caseiro, J. Batista, and C. Sminchisescu · 2012
Earlier work this paper cites.
Ucf101: A dataset of 101 human actions classes from videos in the wild
K. Soomro, A. R. Zamir, and M. Shah · 2012
Earlier work this paper cites.
Fast and scalable polynomial kernels via explicit feature maps
N. Pham and R. Pagh · 2013
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.
Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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.
Bilinear cnn models for fine-grained visual recognition
T.-Y. Lin, A. RoyChowdhury, and S. Maji · 2015
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. Bernstein, et al · 2015
Cited alongside, same era.
Xception: Deep learning with depthwise separable convolutions
F. Chollet · 2016
Cited alongside, same era.
Compact bilinear pooling
Y. Gao, O. Beijbom, N. Zhang, and T. Darrell · 2016
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
Later among the works it cites.
Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Later among the works it cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
Later among the works it cites.
Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2017
Later among the works it cites.
What does it take to generate natural textures?
I. Ustyuzhaninov, W. Brendel, L. A. Gatys, and M. Bethge · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Understanding the effective receptive field in deep convolutional neural networks
W. Luo, Y. Li, R. Urtasun, and R. Zemel · 2016
Cited alongside, same era.
Attention is all you need
N. P. J. U. L. J. A. N. G. L. K. Ashish Vaswani, Noam Shazeer and I. Polosukhin · 2017
Cited alongside, same era.
Kernel pooling for convolutional neural networks
Y. Cui, F. Zhou, J. Wang, X. Liu, Y. Lin, and S. Belongie · 2017
Cited alongside, same era.
X. Wang, R. Girshick, A. Gupta, and K. He · 2017
Later among the works it cites.
Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
Later among the works it cites.
Rethinking spatiotemporal feature learning for video understanding
S. Xie, C. Sun, J. Huang, Z. Tu, and K. Murphy · 2017
Later among the works it cites.
Shufflenet: An extremely efficient convolutional neural network for mobile devices
X. Zhang, X. Zhou, M. Lin, and J. Sun · 2017
Later among the works it cites.
Y. Wu and K. He · 2018
Closest in time.