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In this paper, we propose a deep part-based model (DeePM) for symbiotic object detection and semantic part localization.
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The pascal visual object classes (voc) challenge
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Felzenszwalb, Pedro F, Girshick, Ross B, McAllester, David, and Ramanan, Deva · 2010
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Clustered pose and nonlinear appearance models for human pose estimation
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Recognizing human actions from still images with latent poses
Yang, Weilong, Wang, Yang, and Mori, Greg · 2010
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Latent hierarchical structural learning for object detection
Zhu, Long, Chen, Yuanhao, Yuille, Alan L., and Freeman, William · 2010
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Shared parts for deformable part-based models
Ott, Patrick and Everingham, Mark · 2011
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Articulated pose estimation with flexible mixtures-of-parts
Yang, Yi and Ramanan, Deva · 2011
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Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
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Uijlings, Jasper R. R., van de Sande, Koen E. A., Gevers, Theo, and Smeulders, Arnold W. M · 2013
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Part-based r-cnns for fine-grained category detection
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Fast r-cnn
Girshick, Ross · 2015
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Deformable part models are convolutional neural networks
Girshick, Ross, Iandola, Forrest, Darrell, Trevor, and Malik, Jitendra · 2015
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Rich feature hierarchies for accurate object detection and semantic segmentation
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Deformable part models with cnn features
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End-to-end integration of a convolutional network, deformable parts model and non-maximum suppression
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segdeepm: Exploiting segmentation and context in deep neural networks for object detection
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