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Human actions are comprised of a sequence of poses.
Flowing convnets for human pose estimation in videos
Pfister, T., Charles, J., Zisserman, A.: · 1921
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Modeling deep temporal dependencies with recurrent grammar cells””
Michalski, V., Memisevic, R., Konda, K.: · 1933
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Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions
Chaudhry, R., Ravichandran, A., Hager, G., Vidal, R.: · 1939
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Behavior recognition via sparse spatio-temporal features
Dollár, P., Rabaud, V., Cottrell, G., Belongie, S.: · 2005
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A spatio-temporal descriptor based on 3d-gradients
Klaser, A., Marszałek, M., Schmid, C.: · 2008
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Evaluation of local spatio-temporal features for action recognition
Wang, H., Ullah, M.M., Klaser, A., Laptev, I., Schmid, C.: · 2009
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Deep learning from temporal coherence in video
Mobahi, H., Collobert, R., Weston, J.: · 2009
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Real-time dense and accurate parallel optical flow using cuda
Marzat, J., Dumortier, Y., Ducrot, A.: · 2009
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The PASCAL Visual Object Classes Challenge 2010 (VOC2010) Results
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: · 2010
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Clustered pose and nonlinear appearance models for human pose estimation
Johnson, S., Everingham, M.: · 2010
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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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Learning person-object interactions for action recognition in still images
Delaitre, V., Sivic, J., Laptev, I.: · 2011
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Action recognition from a distributed representation of pose and appearance
Maji, S., Bourdev, L., Malik, J.: · 2011
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Action recognition by dense trajectories
Wang, H., Kläser, A., Schmid, C., Liu, C.L.: · 2011
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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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Learning intermediate-level representations of form and motion from natural movies
Cadieu, C.F., Olshausen, B.A.: · 2012
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Articulated people detection and pose estimation: Reshaping the future
Pishchulin, L., Jain, A., Andriluka, M., Thormählen, T., Schiele, B.: · 2012
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., Malik, J.: · 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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Watching unlabeled video helps learn new human actions from very few labeled snapshots
Chen, C.Y., Grauman, K.: · 2013
Return of the devil in the details: Delving deep into convolutional nets
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: · 2014
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Articulated pose estimation by a graphical model with image dependent pairwise relations
Chen, X., Yuille, A.L.: · 2014
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Girshick, R.: · 2015
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Designing deep networks for surface normal estimation
Wang, X., Fouhey, D., Gupta, A.: · 2015
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Unsupervised visual representation learning by context prediction
Doersch, C., Gupta, A., Efros, A.A.: · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: · 2014
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2014
Cited alongside, same era.
Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., Malik, J.: · 2014
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Deeppose: Human pose estimation via deep neural networks
Toshev, A., Szegedy, C.: · 2014
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Two-stream convolutional networks for action recognition in videos
Simonyan, K., Zisserman, A.: · 2014
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Joint training of a convolutional network and a graphical model for human pose estimation
Tompson, J., Jain, A., Lecun, Y., Bregler, C.: · 2014
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Jayaraman, D., Grauman, K.: · 2015
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Learning to see by moving
Agrawal, P., Carreira, J., Malik, J.: · 2015
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Unsupervised learning of visual representations using videos
Wang, X., Gupta, A.: · 2015
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Unsupervised learning of spatiotemporally coherent metrics
Goroshin, R., Bruna, J., Tompson, J., Eigen, D., LeCun, Y.: · 2015
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Slow and steady feature analysis: Higher order temporal coherence in video
Jayaraman, D., Grauman, K.: · 2015
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Wang, X., Farhadi, A., Gupta, A.: · 2015
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Efficient object localization using convolutional networks
Tompson, J., Goroshin, R., Jain, A., LeCun, Y., Bregler, C.: · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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Recognizing human actions from still images with latent poses
Yang, W., Wang, Y., Mori, G.: · 2037
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