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Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts
(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 ...
Robust Estimation of 3D Human Poses from a Single Image
(Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-10)
Human pose estimation is a key step to action recognition. We propose a method of estimating 3D human poses from a single image, which works in conjunction with an existing 2D pose/joint detector. 3D pose estimation is ...
The Secrets of Salient Object Segmentation
(Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-13)
In this paper we provide an extensive evaluation of fixation prediction and salient object segmentation algorithms as well as statistics of major datasets. Our analysis identifies serious design flaws of existing salient ...
Parsing Occluded People by Flexible Compositions
(Center for Brains, Minds and Machines (CBMM), arXiv, 2015-06-01)
This paper presents an approach to parsing humans when there is significant occlusion. We model humans using a graphical model which has a tree structure building on recent work [32, 6] and exploit the connectivity prior ...