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Understanding temporal information and how the visual world changes over time is a fundamental ability of intelligent systems.
An experimental study of apparent behavior
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Hmdb: a large video database for human motion recognition
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Space-time disarray and visual awareness
J. Koenderink, W. Richards, and A. "van Doorn" · 2012
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UCF101: A dataset of 101 human actions classes from videos in the wild
K. Soomro, A. Roshan Zamir, and M. Shah · 2012
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Thumos challenge: Action recognition with a large number of classes, 2014
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Large-scale video classification with convolutional neural networks
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei · 2014
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Two-stream convolutional networks for action recognition in videos
K. Simonyan and A. Zisserman · 2014
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Activitynet: A large-scale video benchmark for human activity understanding
F. Caba Heilbron, V. Escorcia, B. Ghanem, and J. Carlos Niebles · 2015
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Long-term recurrent convolutional networks for visual recognition and description
J. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell · 2015
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Long-term recurrent convolutional networks for visual recognition and description
J. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell · 2015
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Unsupervised learning of video representations using lstms
N. Srivastava, E. Mansimov, and R. Salakhudinov · 2015
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Learning spatiotemporal features with 3d convolutional networks
D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri · 2015
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Beyond short snippets: Deep networks for video classification
J. Yue-Hei Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Shuffle and learn: Unsupervised learning using temporal order verification
I. Misra, C. L. Zitnick, and M. Hebert · 2016
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Hollywood in homes: Crowdsourcing data collection for activity understanding
The "something something" video database for learning and evaluating visual common sense
R. Goyal, S. E. Kahou, V. Michalski, J. Materzynska, S. Westphal, H. Kim, V. Haenel, I. Fründ, P. Yianilos, M. Mueller-Freitag, F. Hoppe, C. Thurau, I. Bax, and R. Memisevic · 2017
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AVA: A video dataset of spatio-temporally localized atomic visual actions
C. Gu, C. Sun, S. Vijayanarasimhan, C. Pantofaru, D. A. Ross, G. Toderici, Y. Li, S. Ricco, R. Sukthankar, C. Schmid, and J. Malik · 2017
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The kinetics human action video dataset
W. Kay, J. Carreira, K. Simonyan, B. Zhang, C. Hillier, S. Vijayanarasimhan, F. Viola, T. Green, T. Back, P. Natsev, M. Suleyman, and A. Zisserman · 2017
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A closer look at spatiotemporal convolutions for action recognition
D. Tran, H. Wang, L. Torresani, J. Ray, Y. LeCun, and M. Paluri · 2017
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G. A. Sigurdsson, G. Varol, X. Wang, A. Farhadi, I. Laptev, and A. Gupta · 2016
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Temporal segment networks: Towards good practices for deep action recognition
L. Wang, Y. Xiong, Z. Wang, Y. Qiao, D. Lin, X. Tang, and L. Val Gool · 2016
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Quo vadis, action recognition? a new model and the kinetics dataset
J. Carreira and A. Zisserman · 2017
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Generalized rank pooling for activity recognition
A. Cherian, B. Fernando, M. Harandi, and S. Gould · 2017
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Rank pooling for action recognition
B. Fernando, E. Gavves, J. Oramas, A. Ghodrati, and T. Tuytelaars · 2017
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Scaling egocentric vision: The epic-kitchens dataset
D. Damen, H. Doughty, G. M. Farinella, S. Fidler, A. Furnari, E. Kazakos, D. Moltisanti, J. Munro, T. Perrett, W. Price, and M. Wray · 2018
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From lifestyle vlogs to everyday interactions
D. F. Fouhey, W. Kuo, A. A. Efros, and J. Malik · 2018
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What makes a video a video : Analyzing temporal information in video understanding models and datasets
D.-A. Huang, V. Ramanathan, D. Mahajan, L. Torresani, M. Paluri, L. Fei-Fei, and J. C. Niebles · 2018
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Scenes-objects-actions: A multi-task, multi-label video dataset
J. Ray, H. Wang, D. Tran, Y. Wang, M. Feiszli, L. Torresani, and M. Paluri · 2018
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On the integration of optical flow and action recognition
L. Sevilla-Lara, Y. Liao, F. Guney, V. Jampani, A. Geiger, and M. J. Black · 2018
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Moments in time dataset: one million videos for event understanding
M. Monfort, A. Andonian, B. Zhou, K. Ramakrishnan, S. A. Bargal, Y. Yan, L. Brown, Q. Fan, D. Gutfreund, C. Vondrick, et al · 2019
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