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Efficient Pose and Cell Segmentation using Column Generation.
TL;DR: A generic relaxation scheme for solving combinatorial problems using a column generation formulation where the program for generating a column is solved via exact optimization of very small scale integer programs, which results in efficient exploration of the spaces of poses and cells.
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Abstract: We study the problems of multi-person pose segmentation in natural images and instance segmentation in biological images with crowded cells. We formulate these distinct tasks as integer programs where variables correspond to poses/cells. To optimize, we propose a generic relaxation scheme for solving these combinatorial problems using a column generation formulation where the program for generating a column is solved via exact optimization of very small scale integer programs. This results in efficient exploration of the spaces of poses and cells.
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Exploiting skeletal structure in computer vision annotation with Benders decomposition.
TL;DR: This paper applies Bender's decomposition to a typical problem in computer vision where many sub-ILPs are coupled to a master ILP (eg, constructing skeletons), and divides inference problems into a master problem and sub-problems.
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Object Detection with Discriminatively Trained Part-Based Models
TL;DR: An object detection system based on mixtures of multiscale deformable part models that is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges is described.
Reducibility Among Combinatorial Problems
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Fast approximate energy minimization via graph cuts
TL;DR: This work presents two algorithms based on graph cuts that efficiently find a local minimum with respect to two types of large moves, namely expansion moves and swap moves that allow important cases of discontinuity preserving energies.
A discriminatively trained, multiscale, deformable part model
Pedro F. Felzenszwalb,David McAllester,Deva Ramanan +2 more
- 23 Jun 2008
TL;DR: A discriminatively trained, multiscale, deformable part model for object detection, which achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge and outperforms the best results in the 2007 challenge in ten out of twenty categories.
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