Fetching the paper…
Reading the bibliography…
While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex.
Seven strictures on similarity
N. Goodman · 1972
Earlier work this paper cites.
Features of similarity
A. Tversky · 1977
Earlier work this paper cites.
The adaptive character of thought
J. R. Anderson · 1990
Earlier work this paper cites.
Respects for similarity
D. L. Medin, R. L. Goldstone, and D. Gentner · 1993
Earlier work this paper cites.
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
D. Scharstein and R. Szeliski · 2002
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli · 2004
Earlier work this paper cites.
Multiscale structural similarity for image quality assessment
Z. Wang, E. P. Simoncelli, and A. C. Bovik · 2004
Earlier work this paper cites.
Nonintentional similarity processing
A. B. Markman and D. Gentner · 2005
Earlier work this paper cites.
A statistical evaluation of recent full reference image quality assessment algorithms
H. R. Sheikh, M. F. Sabir, and A. C. Bovik · 2006
Earlier work this paper cites.
The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2007
Earlier work this paper cites.
Beyond pixels: exploring new representations and applications for motion analysis
C. Liu et al · 2009
Earlier work this paper cites.
Tid2008-a database for evaluation of full-reference visual quality assessment metrics
N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti · 2009
Earlier work this paper cites.
Complex wavelet structural similarity: A new image similarity index
M. P. Sampat, Z. Wang, S. Gupta, A. C. Bovik, and M. K. Markey · 2009
Earlier work this paper cites.
Most apparent distortion: full-reference image quality assessment and the role of strategy
E. C. Larson and D. M. Chandler · 2010
Earlier work this paper cites.
A database and evaluation methodology for optical flow
S. Baker, D. Scharstein, J. Lewis, S. Roth, M. J. Black, and R. Szeliski · 2011
Earlier work this paper cites.
Learning photographic global tonal adjustment with a database of input / output image pairs
V. Bychkovsky, S. Paris, E. Chan, and F. Durand · 2011
Earlier work this paper cites.
Hdr-vdp-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions
R. Mantiuk, K. J. Kim, A. G. Rempel, and W. Heidrich · 2011
Earlier work this paper cites.
Fsim: A feature similarity index for image quality assessment
L. Zhang, L. Zhang, X. Mou, and D. Zhang · 2011
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.
Ava: A large-scale database for aesthetic visual analysis
N. Murray, L. Marchesotti, and F. Perronnin · 2012
Earlier work this paper cites.
Evaluating amazon’s mechanical turk as a tool for experimental behavioral research
M. J. Crump, J. V. McDonnell, and T. M. Gureckis · 2013
Earlier work this paper cites.
One weird trick for parallelizing convolutional neural networks
A. Krizhevsky · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Learning to see by moving
P. Agrawal, J. Carreira, and J. Malik · 2015
Cited alongside, same era.
Raise: a raw images dataset for digital image forensics
D.-T. Dang-Nguyen, C. Pasquini, V. Conotter, and G. Boato · 2015
Cited alongside, same era.
Hand-held video deblurring via efficient fourier aggregation
M. Delbracio and G. Sapiro · 2015
Cited alongside, same era.
Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
Cited alongside, same era.
Phase-based frame interpolation for video
S. Meyer, O. Wang, H. Zimmer, M. Grosse, and A. Sorkine-Hornung · 2015
Cited alongside, same era.
Context encoders: Feature learning by inpainting
D. Pathak, P. Krähenbühl, J. Donahue, T. Darrell, and A. Efros · 2016
Later among the works it cites.
Using goal-driven deep learning models to understand sensory cortex
D. L. Yamins and J. J. DiCarlo · 2016
Later among the works it cites.
Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
Later among the works it cites.
Ntire 2017 challenge on single image super-resolution: Dataset and study
E. Agustsson and R. Timofte · 2017
Later among the works it cites.
Image quality assessment by comparing cnn features between images
S. Ali Amirshahi, M. Pedersen, and S. X. Yu · 2017
Later among the works it cites.
Eigen-distortions of hierarchical representations
A. Berardino, V. Laparra, J. Ballé, and E. Simoncelli · 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…
Image database tid2013: Peculiarities, results and perspectives
N. Ponomarenko, L. Jin, O. Ieremeiev, V. Lukin, K. Egiazarian, J. Astola, B. Vozel, K. Chehdi, M. Carli, F. Battisti, et al · 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.
Unsupervised learning of visual representations using videos
X. Wang and A. Gupta · 2015
Cited alongside, same era.
Deep networks for image super-resolution with sparse prior
Z. Wang, D. Liu, J. Yang, W. Han, and T. Huang · 2015
Cited alongside, same era.
Generating images with perceptual similarity metrics based on deep networks
A. Dosovitskiy and T. Brox · 2016
Cited alongside, same era.
Image style transfer using convolutional neural networks
L. A. Gatys, A. S. Ecker, and M. Bethge · 2016
Cited alongside, same era.
Photographic image synthesis with cascaded refinement networks
Q. Chen and V. Koltun · 2017
Later among the works it cites.
Adversarial feature learning
J. Donahue, P. Krähenbühl, and T. Darrell · 2017
Later among the works it cites.
Deepsim: Deep similarity for image quality assessment
F. Gao, Y. Wang, P. Li, M. Tan, J. Yu, and Y. Zhu · 2017
Later among the works it cites.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer · 2017
Later among the works it cites.
Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2017
Later among the works it cites.
Deep learning of human visual sensitivity in image quality assessment framework
J. Kim and S. Lee · 2017
Later among the works it cites.
Enhanced deep residual networks for single image super-resolution
B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee · 2017
Later among the works it cites.
Video frame interpolation via adaptive separable convolution
S. Niklaus, L. Mai, and F. Liu · 2017
Later among the works it cites.
Pytorch: Tensors and dynamic neural networks in python with strong gpu acceleration, may 2017
A. Paszke, S. Chintala, R. Collobert, K. Kavukcuoglu, C. Farabet, S. Bengio, I. Melvin, J. Weston, and J. Mariethoz · 2017
Later among the works it cites.
Learning features by watching objects move
D. Pathak, R. Girshick, P. Dollár, T. Darrell, and B. Hariharan · 2017
Later among the works it cites.
Enhancenet: Single image super-resolution through automated texture synthesis
M. S. Sajjadi, B. Schölkopf, and M. Hirsch · 2017
Later among the works it cites.
Deep video deblurring for hand-held cameras
S. Su, M. Delbracio, J. Wang, G. Sapiro, W. Heidrich, and O. Wang · 2017
Later among the works it cites.
Split-brain autoencoders: Unsupervised learning by cross-channel prediction
R. Zhang, P. Isola, and A. A. Efros · 2017
Later among the works it cites.
Learned perceptual image enhancement
H. Talebi and P. Milanfar · 2018
Closest in time.
Nima: Neural image assessment
H. Talebi and P. Milanfar · 2018
Closest in time.