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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
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
56.59 Robust Action Recognition framework using Segmented Block and Distance Mean Histogram of Gradients Approach[Vikas Tripathi, Durgaprasad Gangodkar, Ankush Mittal, Vishnu Kanth] segmented blocks URL Yes 2017
56 Video Classification With CNNs: Using The Codec As A Spatio-Temporal Activity Sensor[Aaron Chadha, Alhabib Abbas and Yiannis Andreopoulos] Codec Based URL No 2017
63 Improved Rank Pooling Strategy for Complex Action Recognition[Eman Mohammadi, Q. M. Jonathan Wu, Mehrdad Saif] Improved Rank Pooling URL Yes 2017
71.7 Learning Long-Term Dependencies for Action Recognition With a Biologically-Inspired Deep Network[Yemin Shi, Yonghong Tian, Yaowei Wang, Wei Zeng, Tiejun Huang] shuttleNet URL Yes 2017
73.6 Pillar Networks++: Distributed non-parametric deep and wide networks[Biswa Sengupta, Yu Qian] Pillar Networks++ (4 Networks) URL No 2017
66.2 Lattice Long Short-Term Memory for Human Action Recognition[Lin Sun, Kui Jia, Kevin Chen, Dit Yan Yeung, Bertram E. Shi, Silvio Savarese] Lattice LSTM URL Yes 2017
69.7 Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection[Mohammadreza Zolfaghari , Gabriel L. Oliveira, Nima Sedaghat, Thomas Brox] Chained Multi-stream Networks URL Yes 2017
82.1 End-to-end Video-level Representation Learning for Action Recognition[Jiagang Zhu, Wei Zou, Zheng Zhu, Lin Li] DTPP (Kinetics pre-training) URL No 2017
69 Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion[Weiyao Lin, Yang Mi, Jianxin Wu, Ke Lu, Hongkai Xiong] CO2FI + ASYN URL No 2017
72.6 Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion[Weiyao Lin, Yang Mi, Jianxin Wu, Ke Lu, Hongkai Xiong] CO2FI + ASYN+IDT URL No 2017
70.2 Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?[Kensho Hara, Hirokatsu Kataoka, Yutaka Satoh] ResNeXt-101 (64f) URL No 2017
69.2 Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification[Xiang Long , Chuang Gan , Gerard de Melo , Jiajun Wu , Xiao Liu , Shilei Wen] Attention Cluster RGB+Flow URL No 2017
70.9 Appearance-and-Relation Networks for Video Classification[Limin Wang , Wei Li , Wen Li ,Luc Van Gool] ARTNet with TSN (Pre-train dataset Kinetics) URL No 2017
72.6 Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion[Weiyao Lin , Yang Mi , Jianxin Wu , Ke Lu , Hongkai Xiong] CO2FI + ASYN + IDT URL No 2017
63.5 Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification[Ali Diba, Mohsen Fayyaz, Vivek Sharma, Amir Hossein Karami, Mohammad Mahdi Arzani, Rahman Yousefzadeh, Luc Van Gool] T3D+TSN ( Three splits) URL No 2017
61.8 Multilayer and Multimodal Fusion of Deep Neural Networks for Video Classification[Xiaodong Yang, Pavlo Molchanov, Jan Kautz] URL Yes 2016
53.9 Action Recognition Using Super Sparse Coding Vector with Spatio-Temporal Awareness[Xiaodong Yang, Ying-Li Tian] URL Yes 2014
70.2 Compressed Video Action Recognition[Chao-Yuan Wu and Manzil Zaheer and Hexiang Hu and R. Manmatha and Alexander J. Smola and Philipp Kraehenbuehl] CoViAR + optical flow URL No 2017
78.7 A Closer Look at Spatiotemporal Convolutions for Action Recognition[Du Tran , Heng Wang , Lorenzo Torresani , Jamie Ray, Yann LeCun, Manohar Paluri] URL Yes 2018
66.2 Activity Recognition based on a Magnitude-Orientation Stream Network[Caetano, C., de Melo, V. H. C., dos Santos, J. A., Schwartz, W. R.] When compared with neural network methods, we were able to outperform many methods using the proposed Magnitude Orientation Stream (MOS). Furthermore, we were able to outperform the original two-stream by 6:6 p.p. just using our temporal stream. URL Yes 2017
80.9 PoTion: Pose MoTion Representation for Action Recognition[Vasileios Choutas, Philippe Weinzaepfel, Jérôme Revaud, Cordelia Schmid] I3D + PoTion URL Yes 2018
81.3 Video Representation Learning Using Discriminative Pooling[Jue Wang, Anoop Cherian, Fatih Porikli, Stephen Gould] SVMP+I3D URL Yes 2018
72.2 Non-Linear Temporal Subspace Representations for Activity Recognition[Anoop Cherian, Suvrit Sra, Stephen Gould, Richard Hartley] KRP-FS + IDT-FV URL Yes 2018
63.8 MiCT: Mixed 3D/2D Convolutional Tube for Human Action Recognition[Yizhou Zhou, Xiaoyan Sun, Zheng-Jun Zha, Wenjun Zeng] MiCT-Net URL Yes 2018
70.5 MiCT: Mixed 3D/2D Convolutional Tube for Human Action Recognition[Yizhou Zhou, Xiaoyan Sun, Zheng-Jun Zha, Wenjun Zeng] Two-stream MiCT-Net URL Yes 2018
74.2 Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition[Shuyang Sun, Zhanghui Kuang, Wanli Ouyang, Lu Sheng, Wei Zhang] RGB + OFF(RGB) + OFF(optical flow) + OFF(raw-OFF) URL Yes 2018
30.7 Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning[Chuang Gan, Boqing Gong, Kun Liu, Hao Su, Leonidas J. Guibas] GG-CNN ImageNet pretraining URL Yes 2018
72.6 End-to-End Learning of Motion Representation for Video Understanding[Lijie Fan, Wenbing Huang, Chuang Gan, Stefano Ermon, Boqing Gong, Junzhou Huang] TVNets + IDT URL Yes 2018
55.4 Learning and Using the Arrow of Time[Donglai Wei, Jospeh Lim, Andrew Zisserman, William T. Freeman] AoT (flow only) URL Yes 2018
66.2 Action recognition by Latent Duration Model[Tingwei Wang, Chuancai Liu and Liantao Wang] the proposed LDM+MIFS URL Yes 2017
69.5 Procedural Generation of Videos to Train Deep Action Recognition Networks[César Roberto de Souza, Adrien Gaidon, Yohann Cabon, Antonio Manuel López Peña] Leveraging our synthetic dataset and multi-task models, we increase the performance from 66.6 to 69.5 URL No 2017

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