Fetching the paper…
Reading the bibliography…
Most action recognition methods base on a) a late aggregation of frame level CNN features using average pooling, max pooling, or RNN, among others, or b) spatio-temporal aggregation via 3D convolutions.
1905
Earlier work this paper cites.
H. Kuehne, H. Jhuang, R. Stiefelhagen, and T. Serre, “HMDB51: A large video database for human motion recognition,” in High Performance Computing in Science and Engineering ‘12
2013
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Two-stream convolutional networks for action recognition in videos,” in Proc. NIPS
2014
Earlier work this paper cites.
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in Proc. CVPR
2014
Earlier work this paper cites.
D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in Proc. ICCV
2015
Earlier work this paper cites.
J. Yue-Hei Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici, “Beyond short snippets: Deep networks for video classification,” in Proc. CVPR
2015
Earlier work this paper cites.
J. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell, “Long-term recurrent convolutional networks for visual recognition and description,” in Proc. CVPR
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” in Proc. ICML
2015
Earlier work this paper cites.
L. Wang, Y. Xiong, Z. Wang, Y. Qiao, D. Lin, X. Tang, and L. Van Gool, “Temporal segment networks: Towards good practices for deep action recognition,” in Proc. ECCV
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. CVPR
2016
Earlier work this paper cites.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proc. CVPR
2016
Cited alongside, same era.
C. Feichtenhofer, A. Pinz, and A. Zisserman, “Convolutional two-stream network fusion for video action recognition,” in Proc. CVPR
2016
Cited alongside, same era.
C. Feichtenhofer, A. Pinz, and R. Wildes, “Spatiotemporal residual networks for video action recognition,” in Proc. NIPS
2016
Cited alongside, same era.
R. Goyal, S. Kahou, V. Michalski, J. Materzynska, S. Westphal, H. Kim, V. Haenel, I. Fruend, P. Yianilos, M. Mueller-Freitag, et al
2017
Cited alongside, same era.
J. Carreira and A. Zisserman, “Quo vadis, action recognition? a new model and the kinetics dataset,” in Proc. CVPR
2017
Cited alongside, same era.
M. Zolfaghari, K. Singh, and T. Brox, “ECO: Efficient Convolutional Network for Online Video Understanding,” in Proc. ECCV
2018
Later among the works it cites.
S. Sun, Z. Kuang, L. Sheng, W. Ouyang, and W. Zhang, “Optical flow guided feature: a fast and robust motion representation for video action recognition,” in Proc. CVPR
2018
Later among the works it cites.
M. Lee, S. Lee, S. Son, G. Park, and N. Kwak, “Motion feature network: Fixed motion filter for action recognition,” in Proc. ECCV
2018
Later among the works it cites.
2018
Later among the works it cites.
D. Dwibedi, P. Sermanet, and J. Tompson, “Temporal Reasoning in Videos using Convolutional Gated Recurrent Units,” in Proc. CVPR Workshops
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Z. Qiu, T. Yao, and T. Mei, “Learning spatio-temporal representation with pseudo-3d residual networks,” in Proc. ICCV
2017
Cited alongside, same era.
J. Ng and L. Davis, “Temporal difference networks for video action recognition,” in Proc. IEEE Winter Conference on Applications of Computer Vision (WACV)
2018
Cited alongside, same era.
D. Tran, H. Wang, L. Torresani, J. Ray, Y. LeCun, and M. Paluri, “A closer look at spatiotemporal convolutions for action recognition,” in Proc. CVPR
2018
Cited alongside, same era.
B. Zhou, A. Andonian, A. Oliva, and A. Torralba, “Temporal relational reasoning in videos,” in Proc. ECCV
2018
Cited alongside, same era.
2018
Later among the works it cites.
D. Damen, H. Doughty, G. Maria Farinella, S. Fidler, A. Furnari, E. Kazakos, D. Moltisanti, J. Munro, T. Perrett, W. Price, and M. Wray, “Scaling egocentric vision: The epic-kitchens dataset,” in Proc. ECCV
2018
Later among the works it cites.
L. Wang, W. Li, W. Li, and L. Van Gool, “Appearance-and-relation networks for video classification,” in Proc. CVPR
2018
Later among the works it cites.
K. Hara, H. Kataoka, and Y. Satoh, “Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?,” in Proc. CVPR
2018
Later among the works it cites.
X. Wang and A. Gupta, “Videos as space-time region graphs,” in Proc. ECCV
2018
Later among the works it cites.