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HMDB

Dataset URL

Description : HMDB collected from various sources, mostly from movies, and a small proportion from public databases such as the Prelinger archive, YouTube and Google videos. The dataset contains 6849 clips divided into 51 action categories, each containing a minimum of 101 clips.

Number of Videos : 6849

Number of Classes : 51

Evaluation: HMDB Eval

Description:

Results

Result Paper Description URL Peer Reviewed Year
Result Paper Description URL Peer Reviewed Year
59.5 Multi-view super vector for action recognition[Cai, Z., Wang, L., Peng, X., Qiao, Y] MVSV URL Yes 2014
61.1 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
61.7 A multi-level representation for action recognition[Wang, L., Qiao, Y., Tang, X] URL Yes 2016
59.4 Two-stream convolutional networks for action recognition in videos[Simonyan, K., Zisserman, A] URL Yes 2014
63.7 Modeling video evolution for action recognition[Fernando, B., Gavves, E., M., J.O., Ghodrati, A.] URL Yes 2015
65.5 Motion part regularization: Improving action recognition via trajectory group selection[Ni, B., Moulin, P., Yang, X., Yan, S] URL Yes 2015
59.1 Human action recognition using factorized spatio-temporal convolutional networks[Sun, L., Jia, K., Yeung, D., Shi, B.E] URL Yes 2015
63.2 Action recognition with trajectory-pooled deepconvolutional descriptors[Wang, L., Qiao, Y., Tang, X] URL Yes 2015
64.8 Long-term temporal convolutions for action recognition[Varol, G., Laptev, I., Schmid, C] URL Yes 2016
63.3 A key volume mining deep framework for action recognition[Zhu, W., Hu, J., Sun, G., Cao, X., Qiao, Y] URL Yes 2016
69.4 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
69.7 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
57.2 Action recognition with improved trajectories[Wang, H., Schmid, C] URL No 2013
58.9 Hidden Two-Stream Convolutional Networks for Action Recognition[Yi Zhu , Zhenzhong Lan ,Shawn Newsam ,Alexander G. Hauptmann ] URL No 2017
70.6 Action Representation Using Classifier Decision Boundaries[Jue Wang , Anoop Cherian , Fatih Porikli , Stephen Gould] URL No 2017
69.8 ActionVLAD: Learning spatio-temporal aggregation for action classification[Rohit Girdhar, Deva Ramanan, Abhinav Gupta, Josef Sivic, Bryan Russell] URL Yes 2017
68.9 Spatiotemporal Pyramid Network for Video Action Recognition[Yunbo Wang, Mingsheng Long, Jianmin Wang, Philip S. Yu] Spatiotemporal Pyramid Network / BN-Inception URL Yes 2017
72.2 Spatiotemporal Multiplier Networks for Video Action Recognition[Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes] Spatiotemporal Multiplier Networks + IDT URL Yes 2017
66.79 Action Recognition with Stacked Fisher Vectors[Xiaojiang Peng, Changqing Zou, Yu Qiao, Qiang Peng] Stacked Fisher Vectors (FV+SFV) URL Yes 2014
67 Generalized Rank Pooling for Activity Recognition[Anoop Cherian, Basura Fernando, Mehrtash Harandi, Stephen Gould] Generalized Rank Pooling + IDT-FV URL Yes 2017
51.4 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
66.4 Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset[Joao Carreira, Andrew Zisserman] Two-Stream I3D, ImageNet pre-training URL Yes 2017
80.7 Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset[Joao Carreira, Andrew Zisserman] Two-Stream I3D, Kinetics pre-training URL Yes 2017
71.8 Pillar Networks for action recognition[Biswa Sengupta, Yu Qian] ResNet/Inception + MKL-SVM URL Yes 2017
73.6 Pillar Networks++: Distributed non-parametric deep and wide networks[Biswa Sengupta, Yu Qian] ResNet/Inception + Gaussian Processes URL No 2017

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