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Visual attention helps achieve robust perception under noise, corruption, and distribution shifts in human vision, which are areas where modern neural networks still fall short.
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Recurrent processing during object recognition
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Recurrent models of visual attention
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Early recurrent feedback facilitates visual object recognition under challenging conditions
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Rethinking attention with performers
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Recurrent neural circuits for contour detection
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Informative dropout for robust representation learning: A shape-bias perspective
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Interpreting attention models with human visual attention in machine reading comprehension
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Towards robust image classification using sequential attention models
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Emerging properties in self-supervised vision transformers
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Convit: Improving vision transformers with soft convolutional inductive biases
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On robustness and transferability of convolutional neural networks
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Rethinking spatial dimensions of vision transformers
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Towards robust vision transformer
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Intriguing properties of vision transformers
Naseer, M., Ranasinghe, K., Khan, S., Hayat, M., Khan, F. S., and Yang, M.-H · 2021
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Vision transformers are robust learners
Paul, S. and Chen, P.-Y · 2021
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Global filter networks for image classification
Rao, Y., Zhao, W., Zhu, Z., Lu, J., and Zhou, J · 2021
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Data augmentation can improve robustness
Rebuffi, S.-A., Gowal, S., Calian, D. A., Stimberg, F., Wiles, O., and Mann, T. A · 2021
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Training data-efficient image transformers & distillation through attention
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Early convolutions help transformers see better
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