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WAPCV
2007
Springer
16 years 22 days ago
Learning to Attend - From Bottom-Up to Top-Down
The control of overt visual attention relies on an interplay of bottom-up and top-down mechanisms. Purely bottom-up models may provide a reasonable account of the looking behaviors...
Hector Jasso, Jochen Triesch
ICMCS
2005
IEEE
105views Multimedia» more  ICMCS 2005»
16 years 7 days ago
Speech-Based Visual Concept Learning Using Wordnet
Modeling visual concepts using supervised or unsupervised machine learning approaches are becoming increasing important for video semantic indexing, retrieval, and filtering appli...
Xiaodan Song, Ching-Yung Lin, Ming-Ting Sun
MLDM
2001
Springer
15 years 11 months ago
Concepts Learning with Fuzzy Clustering and Relevance Feedback
Abstractions and Case-Based Reasoning for Medical Course Data: Two Prognostic Applications . . . . . . . . . . . . . . . . . 23 R. Schmidt and L. Gierl Are Case-Based Reasoning and...
Bir Bhanu, Anlei Dong
AAAI
2007
15 years 9 months ago
Learning Large Scale Common Sense Models of Everyday Life
Recent work has shown promise in using large, publicly available, hand-contributed commonsense databases as joint models that can be used to infer human state from day-to-day sens...
William Pentney, Matthai Philipose, Jeff A. Bilmes...
AAAI
2008
15 years 9 months ago
Structure Learning on Large Scale Common Sense Statistical Models of Human State
Research has shown promise in the design of large scale common sense probabilistic models to infer human state from environmental sensor data. These models have made use of mined ...
William Pentney, Matthai Philipose, Jeff A. Bilmes