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Unsupervised learning of clutter-resistant visual representations from natural videos 

Liao, Qianli; Leibo, Joel Z; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2015-04-27)
Populations of neurons in inferotemporal cortex (IT) maintain an explicit code for object identity that also tolerates transformations of object appearance e.g., position, scale, viewing angle [1, 2, 3]. Though the learning ...
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Semantic Part Segmentation using Compositional Model combining Shape and Appearance 

Wang, Jianyu; Yuille, Alan L. (Center for Brains, Minds and Machines (CBMM), arXiv, 2015-06-08)
In this paper, we study the problem of semantic part segmentation for animals. This is more challenging than standard object detection, object segmentation and pose estimation tasks because semantic parts of animals often ...
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Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts 

Chen, Xianjie; Mottaghi, Roozbeh; Liu, Xiaobai; Fidler, Sanja; Urtasun, Raquel; e.a. (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-10)
Detecting objects becomes difficult when we need to deal with large shape deformation, occlusion and low resolution. We propose a novel approach to i) handle large deformations and partial occlusions in animals (as examples ...

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AuthorYuille, Alan L. (2)Chen, Xianjie (1)Fidler, Sanja (1)Leibo, Joel Z (1)Liao, Qianli (1)Liu, Xiaobai (1)Mottaghi, Roozbeh (1)Poggio, Tomaso (1)Urtasun, Raquel (1)Wang, Jianyu (1)Subject
Artificial Intelligence (3)
Object Recognition (3)
Machine Learning (2)Compositional Models (1)Computer vision (1)Semantic Part Segmentation (1)... View MoreDate Issued2015 (2)2014 (1)Has File(s)Yes (3)

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