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The many successes of deep neural networks (DNNs) over the past decade have largely been driven by computational scale rather than insights from biological intelligence.
How people look at pictures: a study of the psychology and perception in art
Buswell, G.T.: · 1935
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Orienting of attention
Posner, M.I.: · 1980
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The importance of phase in signals
Oppenheim, A.V., Lim, J.S.: · 1981
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Visual coding and the phase structure of natural scenes
Thomson, M.G.: · 1999
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The implementation of visual routines
Roelfsema, P.R., Lamme, V.A., Spekreijse, H.: · 2000
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Ultra-rapid object detection with saccadic eye movements: visual processing speed revisited
Kirchner, H., Thorpe, S.J.: · 2006
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A feedforward architecture accounts for rapid categorization
Serre, T., Oliva, A., Poggio, T.: · 2007
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Rapid object categorization without conscious recognition: aneuropsychological study
Muriel, B., Simon, T., Holle, K.: · 2007
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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
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The role of visual salience in directing eye movements in visual object agnosia
Mannan, S.K., Kennard, C., Husain, M.: · 2009
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The characteristics and limits of rapid visual categorization
Fabre-Thorpe, M.: · 2011
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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How does the brain solve visual object recognition?
DiCarlo, J.J., Zoccolan, D., Rust, N.C.: · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., Zisserman, A.: · 2013
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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., Berg, A.C., Fei-Fei, L.: · 2014
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
Yamins, D.L.K., Hong, H., Cadieu, C.F., Solomon, E.A., Seibert, D., DiCarlo, J.J.: · 2014
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What do saliency models predict?
Koehler, K., Guo, F., Zhang, S., Eckstein, M.P.: · 2014
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Very deep convolutional networks for Large-Scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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SALICON: Saliency in context
Jiang, M., Huang, S., Duan, J., Zhao, Q.: · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: · 2015
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Atoms of recognition in human and computer vision
Ullman, S., Assif, L., Fetaya, E., Harari, D.: · 2016
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How deep is the feature analysis underlying rapid visual categorization?
Eberhardt, S., Cader, J.G., Serre, T.: · 2016
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Inception-v4, Inception-ResNet and the impact of residual connections on learning
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: · 2016
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Xception: Deep learning with depthwise separable convolutions
Chollet, F.: · 2016
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Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: · 2016
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psiturk: An open-source framework for conducting replicable behavioral experiments online
Gureckis, T.M., Martin, J., McDonnell, J., Rich, A.S., Markant, D., Coenen, A., Halpern, D., Hamrick, J.B., Chan, P.: · 2016
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Feature pyramid networks for object detection
Lin, T., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: · 2017
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What are the visual features underlying human versus machine vision?
Linsley, D., Eberhardt, S., Sharma, T., Gupta, P., Serre, T.: · 2017
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Dual path networks
Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., Feng, J.: · 2017
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Central and peripheral vision for scene recognition: A neurocomputational modeling exploration
Wang, P., Cottrell, G.W.: · 2017
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mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: · 2017
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What are the visual features underlying human versus machine vision?
Linsley, D., Eberhardt, S., Sharma, T., Gupta, P., Serre, T.: · 2017
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Superhuman accuracy on the SNEMI3D connectomics challenge
Lee, K., Zung, J., Li, P., Jain, V., Sebastian Seung, H.: · 2017
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Evaluating (and improving) the correspondence between deep neural networks and human representations
Peterson, J.C., Abbott, J.T., Griffiths, T.L.: · 2018
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Large scale image segmentation with structured loss based deep learning for connectome reconstruction
Funke, J., Tschopp, F.D., Grisaitis, W., Sheridan, A., Singh, C., Saalfeld, S., Turaga, S.C.: · 2018
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GOT-10k: A large High-Diversity benchmark for generic object tracking in the wild
Huang, L., Zhao, X., Huang, K.: · 2018
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MobileNetV2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: · 2018
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MnasNet: Platform-Aware neural architecture search for mobile
Designing network design spaces
Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., Dollár, P.: · 2020
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ResNeSt: Split-Attention networks
Zhang, H., Wu, C., Zhang, Z., Zhu, Y., Lin, H., Zhang, Z., Sun, Y., He, T., Mueller, J., Manmatha, R., Li, M., Smola, A.: · 2020
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XNect: real-time multi-person 3D motion capture with a single RGB camera
Mehta, D., Sotnychenko, O., Mueller, F., Xu, W., Elgharib, M., Fua, P., Seidel, H.P., Rhodin, H., Pons-Moll, G., Theobalt, C.: · 2020
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Training data-efficient image transformers & distillation through attention
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H.: · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: · 2020
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Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., Le, Q.V.: · 2018
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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., Brendel, W.: · 2018
Cited alongside, same era.
Recurrent computations for visual pattern completion
Tang, H., Schrimpf, M., Lotter, W., Moerman, C., Paredes, A., Ortega Caro, J., Hardesty, W., Cox, D., Kreiman, G.: · 2018
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The bitter lesson
Sutton, R.: · 2019
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Learning what and where to attend with humans in the loop
Linsley, D., Shiebler, D., Eberhardt, S., Serre, T.: · 2019
Cited alongside, same era.
Recurrence is required to capture the representational dynamics of the human visual system
Kietzmann, T.C., Spoerer, C.J., Sörensen, L.K.A., Cichy, R.M., Hauk, O., Kriegeskorte, N.: · 2019
Cited alongside, same era.
Deep learning: The good, the bad, and the ugly
Serre, T.: · 2019
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: · 2020
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Do adversarially robust ImageNet models transfer better?
Salman, H., Ilyas, A., Engstrom, L., Kapoor, A., Madry, A.: · 2020
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Emergent properties of foveated perceptual systems
Deza, A., Konkle, T.: · 2020
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Integrative benchmarking to advance neurally mechanistic models of human intelligence
Schrimpf, M., Kubilius, J., Lee, M.J., Ratan Murty, N.A., Ajemian, R., DiCarlo, J.J.: · 2020
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Quantifying attention flow in transformers
Abnar, S., Zuidema, W.: · 2020
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Scaling vision transformers
Zhai, X., Kolesnikov, A., Houlsby, N., Beyer, L.: · 2021
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Oculo-retinal dynamics can explain the perception of minimal recognizable configurations
Gruber, L.Z., Ullman, S., Ahissar, E.: · 2021
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What I cannot predict, I do not understand: A Human-Centered evaluation framework for explainability methods
Fel, T., Colin, J., Cadene, R., Serre, T.: · 2021
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Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis
Fel, T., Cadene, R., Chalvidal, M., Cord, M., Vigouroux, D., Serre, T.: · 2021
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Partial success in closing the gap between human and machine vision
Geirhos, R., Narayanappa, K., Mitzkus, B., Thieringer, T., Bethge, M., Wichmann, F.A., Brendel, W.: · 2021
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Tracking without re-recognition in humans and machines
Linsley, D., Malik, G., Kim, J., Govindarajan, L.N., Mingolla, E., Serre, T.: · 2021
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Passive attention in artificial neural networks predicts human visual selectivity
Langlois, T., Zhao, H., Grant, E., Dasgupta, I., Griffiths, T., Jacoby, N.: · 2021
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CYBORG: Blending human saliency into the loss improves deep learning
Boyd, A., Tinsley, P., Bowyer, K., Czajka, A.: · 2021
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When pigs fly: Contextual reasoning in synthetic and natural scenes
Bomatter, P., Zhang, M., Karev, D., others: · 2021
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Transformer tracking
Chen, X., Yan, B., Zhu, J., Wang, D., Yang, X., Lu, H.: · 2021
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Res2Net: A new Multi-Scale backbone architecture
Gao, S.H., Cheng, M.M., Zhao, K., Zhang, X.Y., Yang, M.H., Torr, P.: · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: · 2021
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ConViT: Improving vision transformers with soft convolutional inductive biases
d’Ascoli, S., Touvron, H., Leavitt, M., Morcos, A., Biroli, G., Sagun, L.: · 2021
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MLP-Mixer: An all-MLP architecture for vision
Tolstikhin, I., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., Lucic, M., Dosovitskiy, A.: · 2021
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How to train your ViT? data, augmentation, and regularization in vision transformers
Steiner, A., Kolesnikov, A., Zhai, X., Wightman, R., Uszkoreit, J., Beyer, L.: · 2021
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Superhuman cell death detection with biomarker-optimized neural networks
Linsley, J.W., Linsley, D.A., Lamstein, J., Ryan, G., Shah, K., Castello, N.A., Oza, V., Kalra, J., Wang, S., Tokuno, Z., Javaherian, A., Serre, T., Finkbeiner, S.: · 2021
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A ConvNet for the 2020s
Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: · 2022
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Don’t lie to me! robust and efficient explainability with verified perturbation analysis
Fel, T., Ducoffe, M., Vigouroux, D., Cadene, R., Capelle, M., Nicodeme, C., Serre, T.: · 2022
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Texture-like representation of objects in human visual cortex
Jagadeesh, A.V., Gardner, J.L.: · 2022
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Joint rotational invariance and adversarial training of a dual-stream transformer yields state of the art Brain-Score for area V4
Berrios, W., Deza, A.: · 2022
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Words are all you need? capturing human sensory similarity with textual descriptors
Marjieh, R., van Rijn, P., Sucholutsky, I., Sumers, T.R., Lee, H., Griffiths, T.L., Jacoby, N.: · 2022
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Do better ImageNet classifiers assess perceptual similarity better?
Kumar, M., Houlsby, N., Kalchbrenner, N., Cubuk, E.D.: · 2022
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Human alignment of neural network representations
Muttenthaler, L., Dippel, J., Linhardt, L., Vandermeulen, R.A., Kornblith, S.: · 2022
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