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Many human activities involve object manipulations aiming to modify the object state.
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J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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V. Delaitre, J. Sivic, and I. Laptev · 2011
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Human action recognition by learning bases of action attributes and parts
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C. Doersch, S. Singh, A. Gupta, J. Sivic, and A. A. Efros · 2012
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Detecting activities of daily living in first-person camera views
H. Pirsiavash and D. Ramanan · 2012
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Unsupervised discovery of mid-level discriminative patches
S. Singh, A. Gupta, and A. A. Efros · 2012
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Finding actors and actions in movies
P. Bojanowski, F. Bach, I. Laptev, J. Ponce, C. Schmid, and J. Sivic · 2013
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Modeling actions through state changes
A. Fathi and J. M. Rehg · 2013
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Revisiting Frank-Wolfe: Projection-free sparse convex optimization
M. Jaggi · 2013
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Representing videos using mid-level discriminative patches
A. Jain, A. Gupta, M. Rodriguez, and L. Davis · 2013
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Weakly-supervised alignment of video with text
P. Bojanowski, R. Lajugie, E. Grave, F. Bach, I. Laptev, J. Ponce, and C. Schmid · 2015
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Modeling video evolution for action recognition
B. Fernando, E. Gavves, M. J. Oramas, A. Ghodrati, and T. Tuytelaars · 2015
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Fast R-CNN
R. Girshick · 2015
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P. Isola, J. J. Lim, and E. H. Adelson · 2015
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On the global linear convergence of Frank-Wolfe optimization variants
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What’s cookin’? Interpreting cooking videos using text, speech and vision
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Action recognition with improved trajectories
H. Wang and C. Schmid · 2013
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Weakly supervised action labeling in videos under ordering constraints
P. Bojanowski, R. Lajugie, F. Bach, I. Laptev, J. Ponce, C. Schmid, and J. Sivic · 2014
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You-do, i-learn: Discovering task relevant objects and their modes of interaction from multi-user egocentric video
D. Damen, T. Leelasawassuk, O. Haines, A. Calway, and W. Mayol-Cuevas · 2014
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Efficient image and video co-localization with Frank-Wolfe algorithm
A. Joulin, K. Tang, and L. Fei-Fei · 2014
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Geodesic object proposals
P. Krähenbühl and V. Koltun · 2014
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The SUN attribute database: Beyond categories for deeper scene understanding
G. Patterson, C. Xu, H. Su, and J. Hays · 2014
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Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Unsupervised semantic parsing of video collections
O. Sener, A. Zamir, S. Savarese, and A. Saxena · 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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Unsupervised learning from narrated instruction videos
J.-B. Alayrac, P. Bojanowski, N. Agrawal, I. Laptev, J. Sivic, and S. Lacoste Julien · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Connectionist temporal modeling for weakly supervised action labeling
D.-A. Huang, L. Fei-Fei, and J. C. Niebles · 2016
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Convergence rate of Frank-Wolfe for non-convex objectives
S. Lacoste-Julien · 2016
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Hollywood in homes: Crowdsourcing data collection for activity understanding
G. A. Sigurdsson, G. Varol, X. Wang, A. Farhadi, I. Laptev, and A. Gupta · 2016
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Actions ~ transformations
X. Wang, A. Farhadi, and A. Gupta · 2016
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