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Neural Networks require large amounts of memory and compute to process high resolution images, even when only a small part of the image is actually informative for the task at hand.
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The Caltech-UCSD Birds-200-2011 Dataset
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Jimmy Ba, Volodymyr Mnih, and Koray Kavukcuoglu · 2015
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R. Girshick · 2015
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Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu · 2015
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Multiple granularity descriptors for fine-grained categorization
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Attention to scale: Scale-aware semantic image segmentation
Liang-Chieh Chen, Yi Yang, Jiang Wang, Wei Xu, and Alan L. Yuille · 2016
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Attend, infer, repeat: Fast scene understanding with generative models
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Boosted convolutional neural networks
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Stacked attention networks for image question answering
Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, and Alex Smola · 2016
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Picking deep filter responses for fine-grained image recognition
X. Zhang, H. Xiong, W. Zhou, W. Lin, and Q. Tian · 2016
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Higher-order integration of hierarchical convolutional activations for fine-grained visual categorization
Attention-based deep multiple instance learning
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Tell me where to look: Guided attention inference network
Kunpeng Li, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst, and Yun Fu · 2018
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Learning to navigate for fine-grained classification
Ze Yang, Tiange Luo, Dong Wang, Zhiqiang Hu, Jun Gao, and Liwei Wang · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz · 2018
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Differentiable ranking and sorting using optimal transport
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Saccader: Improving accuracy of hard attention models for vision
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S. Cai, W. Zuo, and L. Zhang · 2017
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Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition
J. Fu, H. Zheng, and T. Mei · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick · 2017
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Learning non-maximum suppression
Jan Hosang, Rodrigo Benenson, and Bernt Schiele · 2017
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Dynamic computational time for visual attention
Z. Li, Y. Yang, X. Liu, F. Zhou, S. Wen, and W. Xu · 2017
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A-lamp: Adaptive layout-aware multi-patch deep convolutional neural network for photo aesthetic assessment
S. Ma, J. Liu, and C. W. Chen · 2017
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Faster R-CNN: towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun · 2017
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Processing megapixel images with deep attention-sampling models
Angelos Katharopoulos and François Fleuret · 2019
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Patch transformer for multi-tagging whole slide histopathology images
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Reparameterizable subset sampling via continuous relaxations
Sang Michael Xie and Stefano Ermon · 2019
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Learning with differentiable perturbed optimizers
Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, and Francis Bach · 2020
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Fast differentiable sorting and ranking
Mathieu Blondel, Olivier Teboul, Quentin Berthet, and Josip Djolonga · 2020
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On the relationship between self-attention and convolutional layers
Jean-Baptiste Cordonnier, Andreas Loukas, and Martin Jaggi · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale, 2020
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2020
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Differentiable top-k operator with optimal transport
Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, and Tomas Pfister · 2020
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MIST: multiple instance spatial transformer network
Baptiste Angles, Yuhe Jin, Simon Kornblith, Andrea Tagliasacchi, and Kwang Moo Yi · 2021
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