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If you're planing to use information provided on this site, please keep in mind that all numbers and papers are added by authors without double checking. We of course try to keep results as accurate as possible, and whenever we got notice of an error it will be fixed, but this does not release you from the obligation of reading the papers and double checking the numbers listed here before using them.
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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