Fetching the paper…
Reading the bibliography…
The visual world is vast and varied, but its variations divide into structured and unstructured factors.
Discharge patterns and functional organization of mammalian retina
S. W. Kuffler · 1953
Earlier work this paper cites.
Analysis of receptive fields of cat retinal ganglion cells
R. W. Rodieck and J. Stone · 1965
Earlier work this paper cites.
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
K. Fukushima · 1980
Earlier work this paper cites.
The laplacian pyramid as a compact image code
P. Burt and E. Adelson · 1983
Earlier work this paper cites.
The structure of images
J. J. Koenderink · 1984
Earlier work this paper cites.
Uniqueness of the gaussian kernel for scale-space filtering
J. Babaud, A. P. Witkin, M. Baudin, and R. O. Duda · 1986
Earlier work this paper cites.
The design and use of steerable filters
W. T. Freeman and E. H. Adelson · 1991
Earlier work this paper cites.
A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information
B. A. Olshausen, C. H. Anderson, and D. C. Van Essen · 1993
Earlier work this paper cites.
Scale-space theory in computer vision
T. Lindeberg · 1994
Earlier work this paper cites.
Deformable kernels for early vision
P. Perona · 1995
Earlier work this paper cites.
The steerable pyramid: A flexible architecture for multi-scale derivative computation
E. P. Simoncelli and W. T. Freeman · 1995
Earlier work this paper cites.
Unconstrained parametrizations for variance-covariance matrices
J. C. Pinheiro and D. M. Bates · 1996
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.
Feature detection with automatic scale selection
T. Lindeberg · 1998
Earlier work this paper cites.
Distinctive image features from scale-invariant keypoints
D. Lowe · 2004
Earlier work this paper cites.
Discrete-Time Signal Processing
A. V. Oppenheim and R. W. Schafer · 2009
Cited alongside, same era.
Invariant scattering convolution networks
J. Bruna and S. Mallat · 2013
Cited alongside, same era.
Some improvements on deep convolutional neural network based image classification
A. G. Howard · 2013
Cited alongside, same era.
Rotation, scaling and deformation invariant scattering for texture discrimination
L. Sifre and S. Mallat · 2013
Cited alongside, same era.
Locally scale-invariant convolutional neural networks
A. Kanazawa, A. Sharma, and D. Jacobs · 2014
Cited alongside, same era.
Semantic image segmentation with deep convolutional nets and fully connected CRFs
Deformable convolutional networks
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei · 2017
Later among the works it cites.
Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
Later among the works it cites.
Dilated residual networks
F. Yu, V. Koltun, and T. Funkhouser · 2017
Later among the works it cites.
Scale-adaptive convolutions for scene parsing
R. Zhang, S. Tang, Y. Zhang, J. Li, and S. Yan · 2017
Later among the works it cites.
Pyramid scene parsing network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
Later among the works it cites.
Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 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…
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2015
Cited alongside, same era.
Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu · 2015
Cited alongside, same era.
The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
Cited alongside, same era.
Dynamic filter networks
B. De Brabandere, X. Jia, T. Tuytelaars, and L. Van Gool · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Structured Receptive Fields in CNNs
J.-H. Jacobsen, J. van Gemert, Z. Lou, and A. W. Smeulders · 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.
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
Later among the works it cites.
Context contrasted feature and gated multi-scale aggregation for scene segmentation
H. Ding, X. Jiang, B. Shuai, A. Q. Liu, and G. Wang · 2018
Later among the works it cites.
Neural architecture search with bayesian optimisation and optimal transport
K. Kandasamy, W. Neiswanger, J. Schneider, B. Poczos, and E. P. Xing · 2018
Later among the works it cites.
In-place activated batchnorm for memory-optimized training of dnns
S. Rota Bulò, L. Porzi, and P. Kontschieder · 2018
Later among the works it cites.
Understanding convolution for semantic segmentation
P. Wang, P. Chen, Y. Yuan, D. Liu, Z. Huang, X. Hou, and G. Cottrell · 2018
Later among the works it cites.
Deep layer aggregation
F. Yu, D. Wang, E. Shelhamer, and T. Darrell · 2018
Later among the works it cites.
Darts: Differentiable architecture search
H. Liu, K. Simonyan, and Y. Yang · 2019
Closest in time.
Making convolutional networks shift-invariant again, 2019
R. Zhang · 2019
Closest in time.