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What is the right way to reason about human activities? What directions forward are most promising? In this work, we analyze the current state of human activity understanding in videos.
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M. D. Zeiler and R. Fergus · 2014
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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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Assessing the significance of performance differences on the pascal voc challenges via bootstrapping
M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2013
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S. Ji, W. Xu, M. Yang, and K. Yu · 2013
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O. Russakovsky, J. Deng, Z. Huang, A. C. Berg, and L. Fei-Fei · 2013
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Action recognition with improved trajectories
H. Wang and C. Schmid · 2013
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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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S. Yeung, O. Russakovsky, N. Jin, M. Andriluka, G. Mori, and L. Fei-Fei · 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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Object detectors emerge in deep scene cnns
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2015
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What makes imagenet good for transfer learning?
M. Huh, P. Agrawal, and A. A. Efros · 2016
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A. Karpathy, J. Johnson, and L. Fei-Fei · 2016
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G. A. Sigurdsson, G. Varol, X. Wang, A. Farhadi, I. Laptev, and A. Gupta · 2016
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S.-E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh · 2016
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Actionvlad: Learning spatio-temporal aggregation for action classification
R. Girdhar, D. Ramanan, A. Gupta, J. Sivic, and B. Russell · 2017
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Asynchronous temporal fields for action recognition
G. A. Sigurdsson, S. Divvala, A. Farhadi, and A. Gupta · 2017
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