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[1] Bolei Zhou,et al. Network Dissection: Quantifying Interpretability of Deep Visual Representations , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Kaiming He,et al. Momentum Contrast for Unsupervised Visual Representation Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[4] Christoph Feichtenhofer,et al. Multiscale Vision Transformers , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[5] Gregory Shakhnarovich,et al. FractalNet: Ultra-Deep Neural Networks without Residuals , 2016, ICLR.
[6] Lu Yuan,et al. Florence: A New Foundation Model for Computer Vision , 2021, ArXiv.
[7] Andrew Zisserman,et al. A Short Note on the Kinetics-700 Human Action Dataset , 2019, ArXiv.
[8] Jonathan Tompson,et al. Unsupervised Learning of Spatiotemporally Coherent Metrics , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[9] Julien Mairal,et al. Emerging Properties in Self-Supervised Vision Transformers , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[10] Alexei A. Efros,et al. Unsupervised Visual Representation Learning by Context Prediction , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[11] Quoc V. Le,et al. ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators , 2020, ICLR.
[12] Martial Hebert,et al. Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification , 2016, ECCV.
[13] Chuang Gan,et al. TSM: Temporal Shift Module for Efficient Video Understanding , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[14] Stella X. Yu,et al. Unsupervised Feature Learning via Non-parametric Instance Discrimination , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[15] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] 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).
[18] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[19] Matthieu Cord,et al. Training data-efficient image transformers & distillation through attention , 2020, ICML.
[20] Susanne Westphal,et al. The “Something Something” Video Database for Learning and Evaluating Visual Common Sense , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[21] Thomas Wolf,et al. VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning , 2021, ArXiv.
[22] Furu Wei,et al. BEiT: BERT Pre-Training of Image Transformers , 2021, ArXiv.
[23] 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).
[24] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[25] Ilya Sutskever,et al. Zero-Shot Text-to-Image Generation , 2021, ICML.
[26] Andrea Vedaldi,et al. Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers , 2021, NeurIPS.
[27] Christian Ledig,et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Furu Wei,et al. VL-BERT: Pre-training of Generic Visual-Linguistic Representations , 2019, ICLR.
[29] Heng Wang,et al. Video Classification With Channel-Separated Convolutional Networks , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[30] Trevor Darrell,et al. Object-Region Video Transformers , 2021, ArXiv.
[31] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[32] Qi Tian,et al. Iterative Reorganization With Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Kaiming He,et al. Improved Baselines with Momentum Contrastive Learning , 2020, ArXiv.
[34] Ashish Vaswani,et al. Self-Attention with Relative Position Representations , 2018, NAACL.
[35] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[36] Cordelia Schmid,et al. ViViT: A Video Vision Transformer , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[37] Frank Hutter,et al. SGDR: Stochastic Gradient Descent with Warm Restarts , 2016, ICLR.
[38] Paolo Favaro,et al. Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles , 2016, ECCV.
[39] Mark Chen,et al. Language Models are Few-Shot Learners , 2020, NeurIPS.
[40] Christoph Feichtenhofer,et al. Improved Multiscale Vision Transformers for Classification and Detection , 2021, ArXiv.
[41] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[42] Song Han,et al. TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Devices , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[43] David G. Lowe,et al. Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[44] Serge J. Belongie,et al. Spatiotemporal Contrastive Video Representation Learning , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Michael S. Ryoo,et al. TokenLearner: What Can 8 Learned Tokens Do for Images and Videos? , 2021, ArXiv.
[46] Yu Cheng,et al. UNITER: UNiversal Image-TExt Representation Learning , 2019, ECCV.
[47] Xinlei Chen,et al. Exploring Simple Siamese Representation Learning , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[48] Alexei A. Efros,et al. What Should Not Be Contrastive in Contrastive Learning , 2020, ICLR.
[49] Stefan Lee,et al. ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks , 2019, NeurIPS.
[50] Stephen Lin,et al. What makes instance discrimination good for transfer learning? , 2020, ICLR.
[51] Thomas Brox,et al. Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[52] Julien Mairal,et al. Unsupervised Learning of Visual Features by Contrasting Cluster Assignments , 2020, NeurIPS.
[53] Nikos Komodakis,et al. Unsupervised Representation Learning by Predicting Image Rotations , 2018, ICLR.
[54] Chen Sun,et al. Revisiting Unreasonable Effectiveness of Data in Deep Learning Era , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[55] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[56] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[57] Christoph Feichtenhofer,et al. X3D: Expanding Architectures for Efficient Video Recognition , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[58] Saining Xie,et al. An Empirical Study of Training Self-Supervised Vision Transformers , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[59] Ross B. Girshick,et al. Masked Autoencoders Are Scalable Vision Learners , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[60] Bolei Zhou,et al. Object Detectors Emerge in Deep Scene CNNs , 2014, ICLR.
[61] Alexei A. Efros,et al. Colorful Image Colorization , 2016, ECCV.
[62] Michal Valko,et al. Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning , 2020, NeurIPS.
[63] K. Simonyan,et al. High-Performance Large-Scale Image Recognition Without Normalization , 2021, ICML.
[64] Geoffrey E. Hinton,et al. A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.
[65] Cordelia Schmid,et al. AVA: A Video Dataset of Spatio-Temporally Localized Atomic Visual Actions , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[66] Oriol Vinyals,et al. Neural Discrete Representation Learning , 2017, NIPS.
[67] Cordelia Schmid,et al. Human Detection Using Oriented Histograms of Flow and Appearance , 2006, ECCV.
[68] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[69] Andrew Zisserman,et al. Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.
[70] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[71] Ildoo Kim,et al. ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision , 2021, ICML.
[72] Frank Hutter,et al. Decoupled Weight Decay Regularization , 2017, ICLR.
[73] Gedas Bertasius,et al. Is Space-Time Attention All You Need for Video Understanding? , 2021, ICML.
[74] Andrew Zisserman,et al. A Short Note about Kinetics-600 , 2018, ArXiv.
[75] Abhinav Gupta,et al. Transitive Invariance for Self-Supervised Visual Representation Learning , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[76] Mark Chen,et al. Generative Pretraining From Pixels , 2020, ICML.
[77] Kaiming He,et al. A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[78] Kaiming He,et al. Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour , 2017, ArXiv.
[79] Zheng Shou,et al. Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[80] Liangzhe Yuan,et al. MoViNets: Mobile Video Networks for Efficient Video Recognition , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[81] Furu Wei,et al. Swin Transformer V2: Scaling Up Capacity and Resolution , 2021, ArXiv.
[82] Stephen Lin,et al. Video Swin Transformer , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[83] Koen E. A. van de Sande,et al. Evaluating Color Descriptors for Object and Scene Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[84] Georg Heigold,et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale , 2021, ICLR.
[85] Andrew Zisserman,et al. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[86] Matthijs Douze,et al. Deep Clustering for Unsupervised Learning of Visual Features , 2018, ECCV.
[87] Fabio Viola,et al. The Kinetics Human Action Video Dataset , 2017, ArXiv.
[88] Alexei A. Efros,et al. Context Encoders: Feature Learning by Inpainting , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[89] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[90] Phillip Isola,et al. Contrastive Multiview Coding , 2019, ECCV.
[91] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[92] Aaron B. Adcock,et al. PyTorchVideo: A Deep Learning Library for Video Understanding , 2021, ACM Multimedia.
[93] Abhinav Gupta,et al. Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases , 2020, NeurIPS.
[94] Jitendra Malik,et al. SlowFast Networks for Video Recognition , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[95] Xiaojie Jin,et al. All Tokens Matter: Token Labeling for Training Better Vision Transformers , 2021, NeurIPS.
[96] Trevor Darrell,et al. Learning Features by Watching Objects Move , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[97] Efstratios Gavves,et al. Self-Supervised Video Representation Learning with Odd-One-Out Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).