Efficient Representation Learning via Adaptive Context Pooling
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[1] Marco Loog,et al. Resolution Learning in Deep Convolutional Networks Using Scale-Space Theory , 2021, IEEE Transactions on Image Processing.
[2] Jiwen Lu,et al. DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification , 2021, NeurIPS.
[3] Lu Yuan,et al. Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[4] Xiang Li,et al. Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[5] Matthieu Cord,et al. Training data-efficient image transformers & distillation through attention , 2020, ICML.
[6] S. Gelly,et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale , 2020, ICLR.
[7] Aurko Roy,et al. Efficient Content-Based Sparse Attention with Routing Transformers , 2020, TACL.
[8] Stephen Lin,et al. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[9] A. Piergiovanni,et al. TokenLearner: Adaptive Space-Time Tokenization for Videos , 2021, NeurIPS.
[10] Nergis Tomen,et al. Deep Continuous Networks , 2024, ICML.
[11] M. Zaheer,et al. Big Bird: Transformers for Longer Sequences , 2020, NeurIPS.
[12] Nikolaos Pappas,et al. Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention , 2020, ICML.
[13] Han Fang,et al. Linformer: Self-Attention with Linear Complexity , 2020, ArXiv.
[14] Santiago Ontañón,et al. ETC: Encoding Long and Structured Data in Transformers , 2020, ArXiv.
[15] Arman Cohan,et al. Longformer: The Long-Document Transformer , 2020, ArXiv.
[16] Liu Yang,et al. Sparse Sinkhorn Attention , 2020, ICML.
[17] Lukasz Kaiser,et al. Reformer: The Efficient Transformer , 2020, ICLR.
[18] Omer Levy,et al. Blockwise Self-Attention for Long Document Understanding , 2019, FINDINGS.
[19] Quoc V. Le,et al. Randaugment: Practical automated data augmentation with a reduced search space , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[20] Zheng Zhang,et al. BP-Transformer: Modelling Long-Range Context via Binary Partitioning , 2019, ArXiv.
[21] Tim Salimans,et al. Axial Attention in Multidimensional Transformers , 2019, ArXiv.
[22] Wenhu Chen,et al. Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting , 2019, NeurIPS.
[23] Edouard Grave,et al. Adaptive Attention Span in Transformers , 2019, ACL.
[24] Trevor Darrell,et al. Blurring the Line Between Structure and Learning to Optimize and Adapt Receptive Fields , 2019, ArXiv.
[25] Ilya Sutskever,et al. Generating Long Sequences with Sparse Transformers , 2019, ArXiv.
[26] Yiming Yang,et al. Transformer-XL: Attentive Language Models beyond a Fixed-Length Context , 2019, ACL.
[27] Yang Li,et al. Area Attention , 2018, ICML.
[28] Noah Constant,et al. Character-Level Language Modeling with Deeper Self-Attention , 2018, AAAI.
[29] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[30] Tong Zhang,et al. Modeling Localness for Self-Attention Networks , 2018, EMNLP.
[31] Abhinav Gupta,et al. Non-local Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[32] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[33] Yi Li,et al. Deformable Convolutional Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[34] Raquel Urtasun,et al. Understanding the Effective Receptive Field in Deep Convolutional Neural Networks , 2016, NIPS.
[35] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Jian Sun,et al. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[37] Trevor Darrell,et al. Beyond spatial pyramids: Receptive field learning for pooled image features , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[38] Andrew Y. Ng,et al. Selecting Receptive Fields in Deep Networks , 2011, NIPS.
[39] Fei-Fei Li,et al. ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[40] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .