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Deep convolutional networks have achieved great success for visual recognition in still images.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: · 1998
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A key volume mining deep framework for action recognition
Zhu, W., Hu, J., Sun, G., Cao, X., Qiao, Y.: · 1999
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A duality based approach for realtime tv- L 1 L^{1} optical flow
Zach, C., Pock, T., Bischof, H.: · 2007
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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.: · 2009
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Modeling temporal structure of decomposable motion segments for activity classification
Niebles, J.C., Chen, C.W., Li, F.F.: · 2010
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Object detection with discriminatively trained part-based models
Felzenszwalb, P.F., Girshick, R.B., McAllester, D.A., Ramanan, D.: · 2010
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HMDB: A large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T.A., Serre, T.: · 2011
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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UCF101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A.R., Shah, M.: · 2012
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Action recognition with improved trajectories
Wang, H., Schmid, C.: · 2013
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Motionlets: Mid-level 3D parts for human motion recognition
Wang, L., Qiao, Y., Tang, X.: · 2013
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Temporal localization of actions with actoms
Gaidon, A., Harchaoui, Z., Schmid, C.: · 2013
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3D convolutional neural networks for human action recognition
Ji, S., Xu, W., Yang, M., Yu, K.: · 2013
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THUMOS challenge: Action recognition with a large number of classes (2013)
Jiang, Y.G., Liu, J., Roshan Zamir, A., Laptev, I., Piccardi, M., Shah, M., Sukthankar, R.: · 2013
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LEAR-INRIA submission for the thumos workshop
Wang, H., Schmid, C.: · 2013
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Two-stream convolutional networks for action recognition in videos
Simonyan, K., Zisserman, A.: · 2014
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Large-scale video classification with convolutional neural networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: · 2014
Cited alongside, same era.
Latent hierarchical model of temporal structure for complex activity classification
Wang, L., Qiao, Y., Tang, X.: · 2014
Cited alongside, same era.
Parsing videos of actions with segmental grammars
Pirsiavash, H., Ramanan, D.: · 2014
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Video action detection with relational dynamic-poselets
Wang, L., Qiao, Y., Tang, X.: · 2014
Cited alongside, same era.
Recognize complex events from static images by fusing deep channels
Xiong, Y., Zhu, K., Lin, D., Tang, X.: · 2015
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Learning spatiotemporal features with 3d convolutional networks
Tran, D., Bourdev, L.D., Fergus, R., Torresani, L., Paluri, M.: · 2015
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Modeling video evolution for action recognition
Fernando, B., Gavves, E., M., J.O., Ghodrati, A., Tuytelaars, T.: · 2015
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Long-term recurrent convolutional networks for visual recognition and description
Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T.: · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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Human action recognition using factorized spatio-temporal convolutional networks
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Visualizing and understanding convolutional networks
Zeiler, M.D., Fergus, R.: · 2014
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Multi-view super vector for action recognition
Cai, Z., Wang, L., Peng, X., Qiao, Y.: · 2014
Cited alongside, same era.
Beyond short snippets: Deep networks for video classification
Ng, J.Y.H., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., Toderici, G.: · 2015
Cited alongside, same era.
Action recognition with trajectory-pooled deep-convolutional descriptors
Wang, L., Qiao, Y., Tang, X.: · 2015
Cited alongside, same era.
Devnet: A deep event network for multimedia event detection and evidence recounting
Gan, C., Wang, N., Yang, Y., Yeung, D.Y., Hauptmann, A.G.: · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2015
Cited alongside, same era.
Sun, L., Jia, K., Yeung, D., Shi, B.E.: · 2015
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Motion part regularization: Improving action recognition via trajectory group selection
Ni, B., Moulin, P., Yang, X., Yan, S.: · 2015
Later among the works it cites.
Real-time action recognition with enhanced motion vector CNNs
Zhang, B., Wang, L., Wang, Z., Qiao, Y., Wang, H.: · 2016
Closest in time.
Long-term temporal convolutions for action recognition
Varol, G., Laptev, I., Schmid, C.: · 2016
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You lead, we exceed: Labor-free video concept learning by jointly exploiting web videos and images
Gan, C., Yao, T., Yang, K., Yang, Y., Mei, T.: · 2016
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Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice
Peng, X., Wang, L., Wang, X., Qiao, Y.: · 2016
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Recognizing an action using its name: A knowledge-based approach
Gan, C., Yang, Y., Zhu, L., Zhao, D., Zhuang, Y.: · 2016
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MoFAP: A multi-level representation for action recognition
Wang, L., Qiao, Y., Tang, X.: · 2016
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