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The goal of this work is to characterize the representational impact that foveation operations have for machine vision systems, inspired by the foveated human visual system, which has higher acuity at the center of gaze and texture-like encoding in the periphery.
The orientation and direction selectivity of cells in macaque visual cortex
De Valois, R. L., Yund, E. W., and Hepler, N · 1982
Earlier work this paper cites.
Real-time foveated multiresolution system for low-bandwidth video communication
Geisler, W. S. and Perry, J. S · 1998
Earlier work this paper cites.
A parametric texture model based on joint statistics of complex wavelet coefficients
Portilla, J. and Simoncelli, E. P · 2000
Earlier work this paper cites.
Multiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., and Bovik, A. C · 2003
Earlier work this paper cites.
When is scene identification just texture recognition?
Renninger, L. W. and Malik, J · 2004
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P · 2004
Earlier work this paper cites.
Translation insensitive image similarity in complex wavelet domain
Wang, Z. and Simoncelli, E. P · 2005
Earlier work this paper cites.
Visual search: The role of peripheral information measured using gaze-contingent displays
Geisler, W. S., Perry, J. S., and Najemnik, J · 2006
Earlier work this paper cites.
Image information and visual quality
Sheikh, H. R. and Bovik, A. C · 2006
Earlier work this paper cites.
Robust object recognition with cortex-like mechanisms
Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., and Poggio, T · 2007
Earlier work this paper cites.
Crowding: A cortical constraint on object recognition
Pelli, D. G · 2008
Earlier work this paper cites.
A summary-statistic representation in peripheral vision explains visual crowding
Balas, B., Nakano, L., and Rosenholtz, R · 2009
Earlier work this paper cites.
The contributions of central versus peripheral vision to scene gist recognition
Larson, A. M. and Loschky, L. C · 2009
Earlier work this paper cites.
Most apparent distortion: full-reference image quality assessment and the role of strategy
Larson, E. C. and Chandler, D. M · 2010
Earlier work this paper cites.
Visual search: A retrospective
Eckstein, M. P · 2011
Earlier work this paper cites.
Metamers of the ventral stream
Freeman, J. and Simoncelli, E · 2011
Earlier work this paper cites.
Visual crowding
Levi, D. M · 2011
Earlier work this paper cites.
Fsim: A feature similarity index for image quality assessment
Zhang, L., Zhang, L., Mou, X., and Zhang, D · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Animal eyes
Land, M. F. and Nilsson, D.-E · 2012
Earlier work this paper cites.
A summary statistic representation in peripheral vision explains visual search
Rosenholtz, R., Huang, J., Raj, A., Balas, B. J., and Ilie, L · 2012
Earlier work this paper cites.
Gradient magnitude similarity deviation: A highly efficient perceptual image quality index
Xue, W., Zhang, L., Mou, X., and Bovik, A. C · 2013
Earlier work this paper cites.
Recurrent models of visual attention
Mnih, V., Heess, N., Graves, A., et al · 2014
Earlier work this paper cites.
Computational role of eccentricity dependent cortical magnification
Poggio, T., Mutch, J., and Isik, L · 2014
Earlier work this paper cites.
Vsi: A visual saliency-induced index for perceptual image quality assessment
Zhang, L., Shen, Y., and Li, H · 2014
Earlier work this paper cites.
Object detectors emerge in deep scene cnns, 2014
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A · 2014
Earlier work this paper cites.
Texture synthesis using convolutional neural networks
Gatys, L. A., Ecker, A. S., and Bethge, M · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
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Foveation-based mechanisms alleviate adversarial examples
Luo, Y., Boix, X., Roig, G., Poggio, T., and Zhao, Q · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
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End-to-end optimized image compression
Ballé, J., Laparra, V., and Simoncelli, E. P · 2016
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Towards metamerism via foveated style transfer
Deza, A., Jonnalagadda, A., and Eckstein, M. P · 2019
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Crowding reveals fundamental differences in local vs. global processing in humans and machines
Doerig, A., Bornet, A., Choung, O. H., and Herzog, M. H · 2019
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Saccader: Improving accuracy of hard attention models for vision
Elsayed, G., Kornblith, S., and Le, Q. V · 2019
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Metamers of neural networks reveal divergence from human perceptual systems
Feather, J., Durango, A., Gonzalez, R., and McDermott, J · 2019
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Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2019
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Can peripheral representations improve clutter metrics on complex scenes?
Deza, A. and Eckstein, M · 2016
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A general account of peripheral encoding also predicts scene perception performance
Ehinger, K. A. and Rosenholtz, R · 2016
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Image style transfer using convolutional neural networks
Gatys, L. A., Ecker, A. S., and Bethge, M · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Laparra, V., Ballé, J., Berardino, A., and Simoncelli, E. P · 2016
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Towards foveated rendering for gaze-tracked virtual reality
Patney, A., Salvi, M., Kim, J., Kaplanyan, A., Wyman, C., Benty, N., Luebke, D., and Lefohn, A · 2016
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Capabilities and limitations of peripheral vision
Rosenholtz, R · 2016
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Kaplanyan, A. S., Sochenov, A., Leimkühler, T., Okunev, M., Goodall, T., and Rufo, G · 2019
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The effects of neural resource constraints on early visual representations
Lindsey, J., Ocko, S. A., Ganguli, S., and Deny, S · 2019
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The contributions of central and peripheral vision to scene-gist recognition with a 180 visual field
Loschky, L. C., Szaffarczyk, S., Beugnet, C., Young, M. E., and Boucart, M · 2019
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Image content is more important than bouma’s law for scene metamers
Wallis, T. S., Funke, C. M., Ecker, A. S., Gatys, L. A., Wichmann, F. A., and Bethge, M · 2019
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Theoretically principled trade-off between robustness and accuracy
Zhang, H., Yu, Y., Jiao, J., Xing, E., El Ghaoui, L., and Jordan, M · 2019
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Simulating a primary visual cortex at the front of cnns improves robustness to image perturbations
Dapello, J., Marques, T., Schrimpf, M., Geiger, F., Cox, D. D., and DiCarlo, J. J · 2020
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Comparison of Image Quality Models for Optimization of Image Processing Systems
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{CUDA}-optimized real-time rendering of a foveated visual system
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