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UCF101

Dataset URL

Description : UCF101 is an action recognition data set of realistic action videos, collected from YouTube, having 101 action categories. This data set is an extension of UCF50 data set which has 50 action categories.

Number of Videos : 13320

Number of Classes : 101

Evaluation: UCF101 Eval

Description: Three splits as defined by authors

Results


Result Paper Description URL Peer Reviewed Year
Result Paper Description URL Peer Reviewed Year
83.5 Multi-view super vector for action recognition[Cai, Z., Wang, L., Peng, X., Qiao, Y] MVSV URL Yes 2014
87.9 Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice[Peng, X., Wang, L., Wang, X., Qiao, Y] URL Yes 2016
88.3 A multi-level representation for action recognition[Wang, L., Qiao, Y., Tang, X] URL Yes 2016
88 Two-stream convolutional networks for action recognition in videos[Simonyan, K., Zisserman, A] URL Yes 2014
88.1 Human action recognition using factorized spatio-temporal convolutional networks[Sun, L., Jia, K., Yeung, D., Shi, B.E] URL Yes 2015
90.3 Action recognition with trajectory-pooled deepconvolutional descriptors[Wang, L., Qiao, Y., Tang, X] URL Yes 2015
91.7 Long-term temporal convolutions for action recognition[Varol, G., Laptev, I., Schmid, C] URL Yes 2016
93.1 A key volume mining deep framework for action recognition[Zhu, W., Hu, J., Sun, G., Cao, X., Qiao, Y] URL Yes 2016
94.2 Temporal Segment Networks: Towards Good Practices for Deep Action Recognition[Limin Wang , Yuanjun Xiong , Zhe Wang , Yu Qiao , Dahua Lin , Xiaoou Tang , and Luc Van Gool] URL Yes 2016
88.6 Beyond short snippets: Deep networks for video classification[Ng, J.Y.H., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., Toderici, G] URL Yes 2015
91.1 Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection[Mohammadreza Zolfaghari , Gabriel L. Oliveira, Nima Sedaghat, and Thomas Brox] URL No 2017
85.2 Learning spatiotemporal features with 3d convolutional networks[Tran, D., Bourdev, L.D., Fergus, R., Torresani, L., Paluri, M] URL No 2015
90.3 Hidden Two-Stream Convolutional Networks for Action Recognition[Yi Zhu , Zhenzhong Lan ,Shawn Newsam ,Alexander G. Hauptmann ] URL No 2017
94.6 Action Representation Using Classifier Decision Boundaries[Jue Wang , Anoop Cherian , Fatih Porikli , Stephen Gould] URL No 2017
93.6 ActionVLAD: Learning spatio-temporal aggregation for action classification[Rohit Girdhar, Deva Ramanan, Abhinav Gupta, Josef Sivic, Bryan Russell] URL Yes 2017
94.6 Spatiotemporal Pyramid Network for Video Action Recognition[Yunbo Wang, Mingsheng Long, Jianmin Wang, Philip S. Yu] Spatiotemporal Pyramid Network / BN-Inception URL Yes 2017
94.9 Spatiotemporal Multiplier Networks for Video Action Recognition[Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes] Spatiotemporal Multiplier Networks + IDT URL Yes 2017
92.3 Generalized Rank Pooling for Activity Recognition[Anoop Cherian, Basura Fernando, Mehrtash Harandi, Stephen Gould] Generalized Rank Pooling + IDT-FV URL Yes 2017
76.3 Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition[ An-An Liu, Yu-Ting Su, Wei-Zhi Nie, Mohan Kankanhalli] HC-MTL with STIP + BOW URL Yes 2017
93.4 Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset[Joao Carreira, Andrew Zisserman] Two-Stream I3D, ImageNet pre-training URL Yes 2017
98 Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset[Joao Carreira, Andrew Zisserman] Two-Stream I3D, Kinetics pre-training URL Yes 2017

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