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This work introduces a novel convolutional network architecture for the task of human pose estimation.
Flowing convnets for human pose estimation in videos
Pfister, T., Charles, J., Zisserman, A.: · 1921
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Learning hierarchical features for scene labeling
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: · 1929
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: · 1998
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Learning to parse images of articulated objects
Ramanan, D.: · 2006
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A discriminatively trained, multiscale, deformable part model
Felzenszwalb, P., McAllester, D., Ramanan, D.: · 2008
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Progressive search space reduction for human pose estimation
Ferrari, V., Marin-Jimenez, M., Zisserman, A.: · 2008
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Poselets: Body part detectors trained using 3d human pose annotations
Bourdev, L., Malik, J.: · 2009
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Clustered pose and nonlinear appearance models for human pose estimation
Johnson, S., Everingham, M.: · 2010
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Learning effective human pose estimation from inaccurate annotation
Johnson, S., Everingham, M.: · 2011
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Torch7: A matlab-like environment for machine learning
Collobert, R., Kavukcuoglu, K., Farabet, C.: · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tieleman, T., Hinton, G.: · 2012
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Modec: Multimodal decomposable models for human pose estimation
Sapp, B., Taskar, B.: · 2013
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Strong appearance and expressive spatial models for human pose estimation
Pishchulin, L., Andriluka, M., Gehler, P., Schiele, B.: · 2013
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Articulated human detection with flexible mixtures of parts
Yang, Y., Ramanan, D.: · 2013
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Human pose estimation using a joint pixel-wise and part-wise formulation
Ladicky, L., Torr, P.H., Zisserman, A.: · 2013
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Real-time human pose recognition in parts from single depth images
Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., Moore, R.: · 2013
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Indoor semantic segmentation using depth information
Couprie, C., Farabet, C., Najman, L., LeCun, Y.: · 2013
Cited alongside, same era.
Joint training of a convolutional network and a graphical model for human pose estimation
Tompson, J.J., Jain, A., LeCun, Y., Bregler, C.: · 2014
Cited alongside, same era.
2d human pose estimation: New benchmark and state of the art analysis
Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: · 2014
Cited alongside, same era.
Deeppose: Human pose estimation via deep neural networks
Toshev, A., Szegedy, C.: · 2014
Cited alongside, same era.
Articulated pose estimation by a graphical model with image dependent pairwise relations
Chen, X., Yuille, A.: · 2014
Cited alongside, same era.
Pose machines: Articulated pose estimation via inference machines
Ramakrishna, V., Munoz, D., Hebert, M., Bagnell, J.A., Sheikh, Y.: · 2014
Parsing occluded people by flexible compositions
Chen, X., Yuille, A.L.: · 2015
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Holistically-nested edge detection
Xie, S., Tu, Z.: · 2015
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
Eigen, D., Fergus, R.: · 2015
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Deepedge: A multi-scale bifurcated deep network for top-down contour detection
Bertasius, G., Shi, J., Torresani, L.: · 2015
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Hypercolumns for object segmentation and fine-grained localization
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: · 2015
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Learning deconvolution network for semantic segmentation
Noh, H., Hong, S., Han, B.: · 2015
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Cited alongside, same era.
Modeep: A deep learning framework using motion features for human pose estimation
Jain, A., Tompson, J., LeCun, Y., Bregler, C.: · 2014
Cited alongside, same era.
Depth map prediction from a single image using a multi-scale deep network
Eigen, D., Puhrsch, C., Fergus, R.: · 2014
Cited alongside, same era.
Recurrent convolutional neural networks for scene labeling
Pinheiro, P., Collobert, R.: · 2014
Cited alongside, same era.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
Cited alongside, same era.
Stacked what-where auto-encoders
Zhao, J., Mathieu, M., Goroshin, R., Lecun, Y.: · 2015
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Deep reflectance maps
Rematas, K., Ritschel, T., Fritz, M., Gavves, E., Tuytelaars, T.: · 2015
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Badrinarayanan, V., Kendall, A., Cipolla, R.: · 2015
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Weakly-supervised disentangling with recurrent transformations for 3d view synthesis
Yang, J., Reed, S.E., Yang, M.H., Lee, H.: · 2015
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Semi-supervised learning with ladder networks
Rasmus, A., Berglund, M., Honkala, M., Valpola, H., Raiko, T.: · 2015
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Convolutional pose machines
Wei, S.E., Ramakrishna, V., Kanade, T., Sheikh, Y.: · 2016
Closest in time.
Human pose estimation with iterative error feedback
Carreira, J., Agrawal, P., Fragkiadaki, K., Malik, J.: · 2016
Closest in time.
Bottom-up and top-down reasoning with hierarchical rectified gaussians
Hu, P., Ramanan, D.: · 2016
Closest in time.
Deep learning for human part discovery in images
Oliveira, G.L., Valada, A., Bollen, C., Burgard, W., Brox, T.: · 2016
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Deep multi-scale video prediction beyond mean square error
Mathieu, M., Couprie, C., LeCun, Y.: · 2016
Closest in time.