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We present an approach for weakly supervised learning of human actions from video transcriptions.
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P. Bojanowski, R. Lajugie, F. Bach, I. Laptev, J. Ponce, C. Schmid, J. Sivic, Weakly supervised action labeling in videos under ordering constraints, in: European Conf. on Computer Vision, 2014
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H. Kuehne, A. Arslan, T. Serre, The language of actions: Recovering the syntax and semantics of goal-directed human activities, in: IEEE Conf. on Computer Vision and Pattern Recognition, 2014, pp. 780–787
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2015
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M. Jain, J. C. van Gemert, C. G. Snoek, What do 15,000 object categories tell us about classifying and localizing actions?, in: IEEE Conf. on Computer Vision and Pattern Recognition, 2015, pp. 46–55
2015
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J. C. van Gemert, M. Jain, E. Gati, C. G. Snoek, Apt: Action localization proposals from dense trajectories, in: British Machine Vision Conference, 2015
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D.-A. Huang, L. Fei-Fei, J. C. Niebles, Connectionist temporal modeling for weakly supervised action labeling, in: European Conf. on Computer Vision, 2016
2016
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A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, L. Fei-Fei, Large-scale video classification with convolutional neural networks, in: IEEE Conf. on Computer Vision and Pattern Recognition, 2014, pp. 1725–1732
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B. Ni, V. R. Paramathayalan, P. Moulin, Multiple granularity analysis for fine-grained action detection, in: IEEE Conf. on Computer Vision and Pattern Recognition, 2014, pp. 756–763
2014
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Y. Cheng, Q. Fan, S. Pankanti, A. Choudhary, Temporal sequence modeling for video event detection, in: IEEE Conf. on Computer Vision and Pattern Recognition, 2014, pp. 2235–2242
2014
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H. Pirsiavash, D. Ramanan, Parsing videos of actions with segmental grammars, in: IEEE Conf. on Computer Vision and Pattern Recognition, 2014, pp. 612–619
2014
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N. N. Vo, A. F. Bobick, From stochastic grammar to bayes network: Probabilistic parsing of complex activity, in: IEEE Conf. on Computer Vision and Pattern Recognition, 2014, pp. 2641–2648
2014
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C. Sun, S. Shetty, R. Sukthankar, R. Nevatia, Temporal localization of fine-grained actions in videos by domain transfer from web images, in: ACM Conf. on Multimedia, 2015, pp. 371–380
2015
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L. Wang, Y. Xiong, Z. Wang, Y. Qiao, D. Lin, X. Tang, L. Van Gool, Temporal segment networks: towards good practices for deep action recognition, in: European Conf. on Computer Vision, 2016, pp. 20–36
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C. Feichtenhofer, A. Pinz, A. Zisserman, Convolutional two-stream network fusion for video action recognition, in: IEEE Conf. on Computer Vision and Pattern Recognition, 2016
2016
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S. Yeung, O. Russakovsky, G. Mori, L. Fei-Fei, End-to-end learning of action detection from frame glimpses in videos, in: IEEE Conf. on Computer Vision and Pattern Recognition, 2016
2016
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A. Richard, J. Gall, Temporal action detection using a statistical language model, in: IEEE Conf. on Computer Vision and Pattern Recognition, 2016
2016
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H. Kuehne, J. Gall, T. Serre, An end-to-end generative framework for video segmentation and recognition, in: IEEE Winter Conference on Applications of Computer Vision, 2016
2016
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J. Alayrac, P. Bojanowski, N. Agrawal, J. Sivic, I. Laptev, S. Lacoste-Julien, Learning from narrated instruction videos, in: IEEE Conf. on Computer Vision and Pattern Recognition, 2016
2016
Closest in time.