Fetching the paper…
Reading the bibliography…
Skeleton-based action recognition has recently received considerable attention.
Microsoft kinect sensor and its effect
Zhengyou Zhang · 2012
Earlier work this paper cites.
Cross-view action modeling, learning and recognition
Jiang Wang, Xiaohan Nie, Yin Xia, Ying Wu, and Song-Chun Zhu · 2014
Earlier work this paper cites.
Hierarchical recurrent neural network for skeleton based action recognition
Yong Du, Wei Wang, and Liang Wang · 2015
Earlier work this paper cites.
Ntu rgb+ d: A large scale dataset for 3d human activity analysis
Amir Shahroudy, Jun Liu, Tian-Tsong Ng, and Gang Wang · 2016
Earlier work this paper cites.
Intel realsense stereoscopic depth cameras, 2017
Leonid Keselman, John Iselin Woodfill, Anders Grunnet-Jepsen, and Achintya Bhowmik · 2017
Earlier work this paper cites.
Ensemble deep learning for skeleton-based action recognition using temporal sliding lstm networks
Inwoong Lee, Doyoung Kim, Seoungyoon Kang, and Sanghoon Lee · 2017
Earlier work this paper cites.
An end-to-end spatio-temporal attention model for human action recognition from skeleton data
Sijie Song, Cuiling Lan, Junliang Xing, Wenjun Zeng, and Jiaying Liu · 2017
Earlier work this paper cites.
View adaptive recurrent neural networks for high performance human action recognition from skeleton data
Pengfei Zhang, Cuiling Lan, Junliang Xing, Wenjun Zeng, Jianru Xue, and Nanning Zheng · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina N. Toutanova · 2018
Earlier work this paper cites.
Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation
Chao Li, Qiaoyong Zhong, Di Xie, and Shiliang Pu · 2018
Earlier work this paper cites.
Part-based graph convolutional network for action recognition
Kalpit Thakkar and PJ Narayanan · 2018
Earlier work this paper cites.
Spatial temporal graph convolutional networks for skeleton-based action recognition
Sijie Yan, Yuanjun Xiong, and Dahua Lin · 2018
Earlier work this paper cites.
Ntu rgb+d 120: A large-scale benchmark for 3d human activity understanding
Jun Liu, Amir Shahroudy, Mauricio Perez, Gang Wang, Ling-Yu Duan, and Alex C Kot · 2019
Earlier work this paper cites.
Two-stream adaptive graph convolutional networks for skeleton-based action recognition
Lei Shi, Yifan Zhang, Jian Cheng, and Hanqing Lu · 2019
Earlier work this paper cites.
An attention enhanced graph convolutional lstm network for skeleton-based action recognition
Chenyang Si, Wentao Chen, Wei Wang, Liang Wang, and Tieniu Tan · 2019
Earlier work this paper cites.
View adaptive neural networks for high performance skeleton-based human action recognition
Pengfei Zhang, Cuiling Lan, Junliang Xing, Wenjun Zeng, Jianru Xue, and Nanning Zheng · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Cited alongside, same era.
Decoupling gcn with dropgraph module for skeleton-based action recognition
Ke Cheng, Yifan Zhang, Congqi Cao, Lei Shi, Jian Cheng, and Hanqing Lu · 2020
Cited alongside, same era.
Skeleton-based action recognition with shift graph convolutional network
Ke Cheng, Yifan Zhang, Xiangyu He, Weihan Chen, Jian Cheng, and Hanqing Lu · 2020
Cited alongside, same era.
Part-level graph convolutional network for skeleton-based action recognition
Linjiang Huang, Yan Huang, Wanli Ouyang, and Liang Wang · 2020
Cited alongside, same era.
Disentangling and unifying graph convolutions for skeleton-based action recognition
Ziyu Liu, Hongwen Zhang, Zhenghao Chen, Zhiyong Wang, and Wanli Ouyang · 2020
Actionclip: A new paradigm for video action recognition
Mengmeng Wang, Jiazheng Xing, and Yong Liu · 2021
Later among the works it cites.
Iip-transformer: Intra-inter-part transformer for skeleton-based action recognition
Qingtian Wang, Jianlin Peng, Shuze Shi, Tingxi Liu, Jiabin He, and Renliang Weng · 2021
Later among the works it cites.
Multi-scale mixed dense graph convolution network for skeleton-based action recognition
Hailun Xia and Xinkai Gao · 2021
Later among the works it cites.
Topology-aware convolutional neural network for efficient skeleton-based action recognition
Kailin Xu, Fanfan Ye, Qiaoyong Zhong, and Di Xie · 2021
Later among the works it cites.
Infogcn: Representation learning for human skeleton-based action recognition
Hyung-gun Chi, Myoung Hoon Ha, Seunggeun Chi, Sang Wan Lee, Qixing Huang, and Karthik Ramani · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Decoupled spatial-temporal attention network for skeleton-based action recognition
Lei Shi, Yifan Zhang, Jian Cheng, and Hanqing Lu · 2020
Cited alongside, same era.
Stronger, faster and more explainable: A graph convolutional baseline for skeleton-based action recognition
Yi-Fan Song, Zhang Zhang, Caifeng Shan, and Liang Wang · 2020
Cited alongside, same era.
Dynamic gcn: Context-enriched topology learning for skeleton-based action recognition
Fanfan Ye, Shiliang Pu, Qiaoyong Zhong, Chao Li, Di Xie, and Huiming Tang · 2020
Cited alongside, same era.
Semantics-guided neural networks for efficient skeleton-based human action recognition
Pengfei Zhang, Cuiling Lan, Wenjun Zeng, Junliang Xing, Jianru Xue, and Nanning Zheng · 2020
Cited alongside, same era.
Channel-wise topology refinement graph convolution for skeleton-based action recognition
Yuxin Chen, Ziqi Zhang, Chunfeng Yuan, Bing Li, Ying Deng, and Weiming Hu · 2021
Cited alongside, same era.
Multi-scale spatial temporal graph convolutional network for skeleton-based action recognition
Zhan Chen, Sicheng Li, Bing Yang, Qinghan Li, and Hong Liu · 2021
Cited alongside, same era.
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, Jakob Uszkoreit, and Neil Houlsby · 2021
Cited alongside, same era.
Closest in time.
Prompting visual-language models for efficient video understanding
Chen Ju, Tengda Han, Kunhao Zheng, Ya Zhang, and Weidi Xie · 2022
Closest in time.
Zero-shot temporal action detection via vision-language prompting
Sauradip Nag, Xiatian Zhu, Yi-Zhe Song, and Tao Xiang · 2022
Closest in time.
Constructing stronger and faster baselines for skeleton-based action recognition
Yi-Fan Song, Zhang Zhang, Caifeng Shan, and Liang Wang · 2022
Closest in time.
Motionclip: Exposing human motion generation to clip space
Guy Tevet, Brian Gordon, Amir Hertz, Amit H Bermano, and Daniel Cohen-Or · 2022
Closest in time.
Unified contrastive learning in image-text-label space, 2022
Jianwei Yang, Chunyuan Li, Pengchuan Zhang, Bin Xiao, Ce Liu, Lu Yuan, and Jianfeng Gao · 2022
Closest in time.
Conditional prompt learning for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
Closest in time.
Learning to prompt for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
Closest in time.
Action-gpt: Leveraging large-scale language models for improved and generalized action generation
Sai Shashank Kalakonda, Shubh Maheshwari, and Ravi Kiran Sarvadevabhatla · 2023
Closest in time.
Hake: A knowledge engine foundation for human activity understanding
Yong-Lu Li, Xinpeng Liu, Xiaoqian Wu, Yizhuo Li, Zuoyu Qiu, Liang Xu, Yue Xu, Hao-Shu Fang, and Cewu Lu · 2023
Closest in time.
Visual classification via description from large language models
Sachit Menon and Carl Vondrick · 2023
Closest in time.