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» MIML: A Framework for Learning with Ambiguous Objects
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CVPR
2012
IEEE
12 years 6 days ago
Batch mode Adaptive Multiple Instance Learning for computer vision tasks
Multiple Instance Learning (MIL) has been widely exploited in many computer vision tasks, such as image retrieval, object tracking and so on. To handle ambiguity of instance label...
Wen Li, Lixin Duan, Ivor Wai-Hung Tsang, Dong Xu
WACV
2008
IEEE
14 years 4 months ago
Likelihood Map Fusion for Visual Object Tracking
Visual object tracking can be considered as a figure-ground classification task. In this paper, different features are used to generate a set of likelihood maps for each pixel i...
Zhaozheng Yin, Fatih Porikli, Robert T. Collins
CVPR
2008
IEEE
14 years 11 months ago
A hierarchical and contextual model for aerial image understanding
In this paper we present a novel method for parsing aerial images with a hierarchical and contextual model learned in a statistical framework. We learn hierarchies at the scene an...
Jake Porway, Kristy Wang, Benjamin Yao, Song Chun ...
AAI
1999
125views more  AAI 1999»
13 years 9 months ago
Deictic Believability: Coordinated Gesture, Locomotion, and Speech in Lifelike Pedagogical Agents
Lifelike animated agents for knowledge-based learning environments can provide timely, customized advice to support students' problem solving. Because of their strong visual ...
James C. Lester, Jennifer L. Voerman, Stuart G. To...
ICCV
2003
IEEE
14 years 11 months ago
Ranking Prior Likelihood Distributions for Bayesian Shape Localization Framework
In this paper, we formulate the shape localization problem in the Bayesian framework. In the learning stage, we propose the Constrained RankBoost approach to model the likelihood ...
Shuicheng Yan, Mingjing Li, HongJiang Zhang, QianS...