暂无分享,去创建一个
[1] Zhuowen Tu,et al. Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Yi Yang,et al. Random Erasing Data Augmentation , 2017, AAAI.
[3] Enhua Wu,et al. Transformer in Transformer , 2021, NeurIPS.
[4] Kaiming He,et al. Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] In-So Kweon,et al. CBAM: Convolutional Block Attention Module , 2018, ECCV.
[6] Irwan Bello. LambdaNetworks: Modeling Long-Range Interactions Without Attention , 2021, ICLR.
[7] Seong Joon Oh,et al. CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[8] Stephen Lin,et al. Local Relation Networks for Image Recognition , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[9] Quanfu Fan,et al. More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation , 2019, NeurIPS.
[10] Enhua Wu,et al. Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[11] Jitendra Malik,et al. SlowFast Networks for Video Recognition , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[12] Jordi Gonzàlez,et al. A coarse-to-fine approach for fast deformable object detection , 2011, CVPR 2011.
[13] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[14] Edward H. Adelson,et al. PYRAMID METHODS IN IMAGE PROCESSING. , 1984 .
[15] Marcel Salathé,et al. Using Deep Learning for Image-Based Plant Disease Detection , 2016, Front. Plant Sci..
[16] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[17] Quoc V. Le,et al. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.
[18] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] C. V. Jawahar,et al. Cats and dogs , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[20] Shuicheng Yan,et al. Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet , 2021, ArXiv.
[21] Qilong Wang,et al. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Torsten Hoefler,et al. Augment Your Batch: Improving Generalization Through Instance Repetition , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Kaiming He,et al. Designing Network Design Spaces , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Vijay Vasudevan,et al. Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[25] Fei-Fei Li,et al. ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[26] Georg Heigold,et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale , 2021, ICLR.
[27] Rogério Schmidt Feris,et al. A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection , 2016, ECCV.
[28] Jitendra Malik,et al. Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..
[29] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[30] Andrew Zisserman,et al. Perceiver: General Perception with Iterative Attention , 2021, ICML.
[31] Chen Sun,et al. Revisiting Unreasonable Effectiveness of Data in Deep Learning Era , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[32] Rogério Schmidt Feris,et al. Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition , 2018, ICLR.
[33] Ling Shao,et al. Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions , 2021, ArXiv.
[34] HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[36] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[37] Ronald M. Summers,et al. ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.
[38] Matthieu Cord,et al. Training data-efficient image transformers & distillation through attention , 2020, ICML.
[39] Abhinav Gupta,et al. Non-local Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[40] Jia Deng,et al. Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.
[41] Jian Yang,et al. Selective Kernel Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[42] Tae Hyun Kim,et al. Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[43] Quoc V. Le,et al. Attention Augmented Convolutional Networks , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[44] Vladlen Koltun,et al. Exploring Self-Attention for Image Recognition , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Pieter Abbeel,et al. Bottleneck Transformers for Visual Recognition , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Shuicheng Yan,et al. Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks With Octave Convolution , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[47] Songfan Yang,et al. Multi-scale Recognition with DAG-CNNs , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[48] 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).
[49] Qiang Liu,et al. Centroid Transformers: Learning to Abstract with Attention , 2021, ArXiv.
[50] Ashish Vaswani,et al. Stand-Alone Self-Attention in Vision Models , 2019, NeurIPS.