Paper Overview


Paper Title
Multi-view super vector for action recognition [Cai, Z., Wang, L., Peng, X., Qiao, Y]
Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice [Peng, X., Wang, L., Wang, X., Qiao, Y]
A multi-level representation for action recognition [Wang, L., Qiao, Y., Tang, X]
Two-stream convolutional networks for action recognition in videos [Simonyan, K., Zisserman, A]
Modeling video evolution for action recognition [Fernando, B., Gavves, E., M., J.O., Ghodrati, A.]
Motion part regularization: Improving action recognition via trajectory group selection [Ni, B., Moulin, P., Yang, X., Yan, S]
Human action recognition using factorized spatio-temporal convolutional networks [Sun, L., Jia, K., Yeung, D., Shi, B.E]
Action recognition with trajectory-pooled deepconvolutional descriptors [Wang, L., Qiao, Y., Tang, X]
Long-term temporal convolutions for action recognition [Varol, G., Laptev, I., Schmid, C]
A key volume mining deep framework for action recognition [Zhu, W., Hu, J., Sun, G., Cao, X., Qiao, Y]
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]
Beyond short snippets: Deep networks for video classification [Ng, J.Y.H., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., Toderici, G]
Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection [Mohammadreza Zolfaghari , Gabriel L. Oliveira, Nima Sedaghat, and Thomas Brox]
Action recognition with improved trajectories [Wang, H., Schmid, C]
Learning spatiotemporal features with 3d convolutional networks [Tran, D., Bourdev, L.D., Fergus, R., Torresani, L., Paluri, M]
Hidden Two-Stream Convolutional Networks for Action Recognition [Yi Zhu , Zhenzhong Lan ,Shawn Newsam ,Alexander G. Hauptmann ]
Action Representation Using Classifier Decision Boundaries [Jue Wang , Anoop Cherian , Fatih Porikli , Stephen Gould]
ActionVLAD: Learning spatio-temporal aggregation for action classification [Rohit Girdhar, Deva Ramanan, Abhinav Gupta, Josef Sivic, Bryan Russell]
The Language of Actions: Recovering the Syntax and Semantics of Goal-Directed Human Activities [H. Kuehne, A. B. Arslan and T. Serre]
Towards understanding action recognition [H. Jhuang and J. Gall and S. Zuffi and C. Schmid and M. J. Black]
Weakly Supervised Action Labeling in Videos Under Ordering Constraints [Bojanowski, Piotr and Lajugie, R'emi and Bach, Francis and Laptev, Ivan and Ponce, Jean and Schmid, Cordelia and Sivic, Josef]
Weakly supervised action labeling in videos under ordering constraints [P. Bojanowski, R. Lajugie, F. Bach, I. Laptev, J. Ponce, C. Schmid, and J. Sivic]
Weakly supervised learning of actions from transcripts. [H. Kuehne, A. Richard, and J. Gall]
Connectionist temporal modeling for weakly supervised action labeling [D.-A. Huang, L. Fei-Fei, and J. C. Niebles]
Generalized Rank Pooling for Activity Recognition [Anoop Cherian, Basura Fernando, Mehrtash Harandi, Stephen Gould]
Action Representation Using Classifier Decision Boundarie s [Yi Zhu, Zhenzhong Lan, Shawn Newsam, Alexander G. Hauptmann]
Spatiotemporal Pyramid Network for Video Action Recognition [Yunbo Wang, Mingsheng Long, Jianmin Wang, Philip S. Yu]
Weakly supervised learning of actions from transcripts [Hilde Kuehne, Alexander Richard, Juergen Gall]
Multi-region two-stream R-CNN for action detection [Xiaojiang Peng, Cordelia Schmid]
Finding Action Tubes [Georgia Gkioxari, Jitendra Malik]
An end-to-end generative framework for video segmentation and recognition [Hilde Kuehne, Juergen Gall, Thomas Serre]
Weakly Supervised Action Learning with RNN based Fine-to-coarse Modeling [Alexander Richard, Hilde Kuehne, Juergen Gall]
The Language of Actions: Recovering the Syntax and Semantics of Goal-Directed Human Activities [H. Kuehne, A. B. Arslan and T. Serre]
Spatiotemporal Multiplier Networks for Video Action Recognition [Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes]
P-CNN: Pose-based CNN Features for Action Recognition [Guilhem Cheron, Ivan Laptev, Cordelia Schmid]
Action Recognition with Stacked Fisher Vectors [Xiaojiang Peng, Changqing Zou, Yu Qiao, Qiang Peng]
Higher-order Pooling of CNN Features via Kernel Linearization for Action Recognition [A. Cherian, P. Koniusz, S. Gould]
Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition [ An-An Liu, Yu-Ting Su, Wei-Zhi Nie, Mohan Kankanhalli]
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset [Joao Carreira, Andrew Zisserman]
Pillar Networks for action recognition [Biswa Sengupta, Yu Qian]
Pillar Networks++: Distributed non-parametric deep and wide networks [Biswa Sengupta, Yu Qian]