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We present a new architecture for end-to-end sequence learning of actions in video, we call VideoLSTM.
Long short-term memory
Hochreiter, S., Schmidhuber, J.: · 1997
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: · 1998
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A duality based approach for realtime tv-l1 optical flow
Zach, C., Pock, T., Bischof, H.: · 2007
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Action classification in soccer videos with long short-term memory recurrent neural networks
Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., Baskurt, A.: · 2010
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Sequential deep learning for human action recognition
Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., Baskurt, A.: · 2011
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HMDB: a large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: · 2011
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Action bank: A high-level representation of activity in video
Sadanand, S., Corso, J.J.: · 2012
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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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Rmsprop: Divide the gradient by a running average of its recent magnitude
Tieleman, T., Hinton, G.: · 2012
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Better exploiting motion for better action recognition
Jain, M., Jégou, H., Bouthemy, P.: · 2013
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Action Recognition with Improved Trajectories
Wang, H., 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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Large-scale video classification with convolutional neural networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: · 2014
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Two-stream convolutional networks for action recognition in videos
Simonyan, K., Zisserman, A.: · 2014
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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.: · 2014
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Action recognition with stacked fisher vectors
Peng, X., Zou, C., Qiao, Y., Peng, Q.: · 2014
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Action localization by tubelets from motion
Jain, M., van Gemert, J.C., Jégou, H., Bouthemy, P., Snoek, C.G.M.: · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: · 2014
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: · 2015
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Beyond gaussian pyramid: Multi-skip feature stacking for action recognition
Lan, Z., Lin, M., Li, X., Hauptmann, A.G., Raj, B.: · 2015
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Action recognition with trajectory-pooled deep-convolutional descriptors
Wang, L., Qiao, Y., Tang, X.: · 2015
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What do 15,000 object categories tell us about classifying and localizing actions?
Jain, M., van Gemert, J.C., Snoek, C.G.M.: · 2015
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Deep visual-semantic alignments for generating image descriptions
Karpathy, A., Fei-Fei, L.: · 2015
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Cited alongside, same era.
Modeling video evolution for action recognition
Fernando, B., Gavves, E., Oramas, J., Ghodrati, A., Tuytelaars, T.: · 2015
Cited alongside, same era.
Learning spatiotemporal features with 3d convolutional networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: · 2015
Cited alongside, same era.
Long-term recurrent convolutional networks for visual recognition and description
Donahue, J., Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T.: · 2015
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Beyond short snippets: Deep networks for video classification
Yue-Hei Ng, J., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., Toderici, G.: · 2015
Cited alongside, same era.
Unsupervised learning of video representations using lstms
Srivastava, N., Mansimov, E., Salakhutdinov, R.: · 2015
Cited alongside, same era.
Show, attend and tell: Neural image caption generation with visual attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., Bengio, Y.: · 2015
Cited alongside, same era.
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Guiding the long-short term memory model for image caption generation
Jia, X., Gavves, E., Fernando, B., Tuytelaars, T.: · 2015
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Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., Bengio, Y.: · 2015
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Fast action proposals for human action detection and search
Yu, G., Yuan, J.: · 2015
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APT: Action localization proposals from dense trajectories
van Gemert, J.C., Jain, M., Gati, E., Snoek, C.G.M.: · 2015
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Learning to track for spatio-temporal action localization
Weinzaepfel, P., Harchaoui, Z., Schmid, C.: · 2015
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Modeling spatial-temporal clues in a hybrid deep learning framework for video classification
Wu, Z., Wang, X., Jiang, Y.G., Ye, H., Xue, X.: · 2015
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Action recognition using visual attention
Sharma, S., Kiros, R., Salakhutdinov, R.: · 2016
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