Fetching the paper…
Reading the bibliography…
Pure vision transformer architectures are highly effective for short video classification and action recognition tasks.
Learning internal representations by error propagation
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1985
Earlier work this paper cites.
Fisher kernels on visual vocabularies for image categorization
Florent Perronnin and Christopher Dance · 2007
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Actions in context
Marcin Marszalek, Ivan Laptev, and Cordelia Schmid · 2009
Earlier work this paper cites.
Aggregating local descriptors into a compact image representation
Hervé Jégou, Matthijs Douze, Cordelia Schmid, and Patrick Pérez · 2010
Earlier work this paper cites.
Movie genre classification via scene categorization
Howard Zhou, Tucker Hermans, Asmita V Karandikar, and James M Rehg · 2010
Earlier work this paper cites.
Sequential deep learning for human action recognition
Moez Baccouche, Franck Mamalet, Christian Wolf, Christophe Garcia, and Atilla Baskurt · 2011
Earlier work this paper cites.
Movie genre classification using svm with audio and video features
Yin-Fu Huang and Shih-Hao Wang · 2012
Earlier work this paper cites.
3d convolutional neural networks for human action recognition
Shuiwang Ji, Wei Xu, Ming Yang, and Kai Yu · 2012
Earlier work this paper cites.
Large-scale video classification with convolutional neural networks
Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei · 2014
Earlier work this paper cites.
Two-stream convolutional networks for action recognition in videos
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Long-term recurrent convolutional networks for visual recognition and description
Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
Earlier work this paper cites.
Learning spatiotemporal features with 3d convolutional networks
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri · 2015
Earlier work this paper cites.
Modeling spatial-temporal clues in a hybrid deep learning framework for video classification
Zuxuan Wu, Xi Wang, Yu-Gang Jiang, Hao Ye, and Xiangyang Xue · 2015
Earlier work this paper cites.
Beyond short snippets: Deep networks for video classification
Joe Yue-Hei Ng, Matthew Hausknecht, Sudheendra Vijayanarasimhan, Oriol Vinyals, Rajat Monga, and George Toderici · 2015
Earlier work this paper cites.
Youtube-8m: A large-scale video classification benchmark
Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Paul Natsev, George Toderici, Balakrishnan Varadarajan, and Sudheendra Vijayanarasimhan · 2016
Earlier work this paper cites.
Word2visualvec: Image and video to sentence matching by visual feature prediction
Jianfeng Dong, Xirong Li, and Cees GM Snoek · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Quo vadis, action recognition? a new model and the kinetics dataset
Joao Carreira and Andrew Zisserman · 2017
Earlier work this paper cites.
Attention-based multimodal fusion for video description
Chiori Hori, Takaaki Hori, Teng-Yok Lee, Ziming Zhang, Bret Harsham, John R Hershey, Tim K Marks, and Kazuhiko Sumi · 2017
Earlier work this paper cites.
Learning spatio-temporal representation with pseudo-3d residual networks
Zhaofan Qiu, Ting Yao, and Tao Mei · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Residual attention network for image classification
Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, and Xiaoou Tang · 2017
Cited alongside, same era.
Rethinking the faster r-cnn architecture for temporal action localization
Yu-Wei Chao, Sudheendra Vijayanarasimhan, Bryan Seybold, David A Ross, Jia Deng, and Rahul Sukthankar · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Spatial-temporal pyramid based convolutional neural network for action recognition
Zhenxing Zheng, Gaoyun An, Dapeng Wu, and Qiuqi Ruan · 2019
Later among the works it cites.
Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Later among the works it cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Later among the works it cites.
X3d: Expanding architectures for efficient video recognition
Christoph Feichtenhofer · 2020
Later among the works it cites.
Movienet: A holistic dataset for movie understanding
Qingqiu Huang, Yu Xiong, Anyi Rao, Jiaze Wang, and Dahua Lin · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
From lifestyle vlogs to everyday interactions
David F Fouhey, Wei-cheng Kuo, Alexei A Efros, and Jitendra Malik · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Cited alongside, same era.
Learning joint embedding with multimodal cues for cross-modal video-text retrieval
Niluthpol Chowdhury Mithun, Juncheng Li, Florian Metze, and Amit K Roy-Chowdhury · 2018
Cited alongside, same era.
A closer look at spatiotemporal convolutions for action recognition
Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, and Manohar Paluri · 2018
Cited alongside, same era.
Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification
Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu, and Kevin Murphy · 2018
Cited alongside, same era.
Modeling localness for self-attention networks
Baosong Yang, Zhaopeng Tu, Derek F Wong, Fandong Meng, Lidia S Chao, and Tong Zhang · 2018
Cited alongside, same era.
Later among the works it cites.
Attentionnas: Spatiotemporal attention cell search for video classification
Xiaofang Wang, Xuehan Xiong, Maxim Neumann, AJ Piergiovanni, Michael S Ryoo, Anelia Angelova, Kris M Kitani, and Wei Hua · 2020
Later among the works it cites.
Vivit: A video vision transformer
Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid · 2021
Later among the works it cites.
Is space-time attention all you need for video understanding?
Gedas Bertasius, Heng Wang, and Lorenzo Torresani · 2021
Later among the works it cites.
Convit: Improving vision transformers with soft convolutional inductive biases
Stéphane d’Ascoli, Hugo Touvron, Matthew Leavitt, Ari Morcos, Giulio Biroli, and Levent Sagun · 2021
Later among the works it cites.
Multiscale vision transformers
Haoqi Fan, Bo Xiong, Karttikeya Mangalam, Yanghao Li, Zhicheng Yan, Jitendra Malik, and Christoph Feichtenhofer · 2021
Later among the works it cites.
Rethinking genre classification with fine grained semantic clustering
Edward Fish, Jon Weinbren, and Andrew Gilbert · 2021
Later among the works it cites.
Pytorch library for cam methods
Jacob Gildenblat and contributors · 2021
Later among the works it cites.
Anticipative video transformer
Rohit Girdhar and Kristen Grauman · 2021
Later among the works it cites.
Cmt: Convolutional neural networks meet vision transformers
Jianyuan Guo, Kai Han, Han Wu, Chang Xu, Yehui Tang, Chunjing Xu, and Yunhe Wang · 2021
Later among the works it cites.
Daniel Neimark, Omri Bar, Maya Zohar, and Dotan Asselmann · 2021
Later among the works it cites.
Actor-context-actor relation network for spatio-temporal action localization
Junting Pan, Siyu Chen, Mike Zheng Shou, Yu Liu, Jing Shao, and Hongsheng Li · 2021
Later among the works it cites.
Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
Later among the works it cites.
Towards long-form video understanding
Chao-Yuan Wu and Philipp Krahenbuhl · 2021
Later among the works it cites.
Tokens-to-token vit: Training vision transformers from scratch on imagenet
Li Yuan, Yunpeng Chen, Tao Wang, Weihao Yu, Yujun Shi, Zihang Jiang, Francis EH Tay, Jiashi Feng, and Shuicheng Yan · 2021
Later among the works it cites.
Vidtr: Video transformer without convolutions
Yanyi Zhang, Xinyu Li, Chunhui Liu, Bing Shuai, Yi Zhu, Biagio Brattoli, Hao Chen, Ivan Marsic, and Joseph Tighe · 2021
Later among the works it cites.