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CVPR
2007
IEEE
16 years 9 months ago
Adaptive Patch Features for Object Class Recognition with Learned Hierarchical Models
We present a hierarchical generative model for object recognition that is constructed by weakly-supervised learning. A key component is a novel, adaptive patch feature whose width...
Fabien Scalzo, Justus H. Piater
CVPR
2010
IEEE
16 years 4 months ago
Learning Full Pairwise Affinities for Spectral Segmentation
This paper studies the problem of learning a full range of pairwise affinities gained by integrating local grouping cues for spectral segmentation. The overall quality of the spect...
Tae Hoon Kim (Seoul National University), Kyoung M...
SCESM
2006
ACM
269views Algorithms» more  SCESM 2006»
16 years 1 months ago
Inferring operational requirements from scenarios and goal models using inductive learning
Goal orientation is an increasingly recognised Requirements Engineering paradigm. However, integration of goal modelling with operational models remains an open area for which the...
Dalal Alrajeh, Alessandra Russo, Sebastián ...
ICCV
2005
IEEE
16 years 1 months ago
Learning Models for Predicting Recognition Performance
This paper addresses one of the fundamental problems encountered in performance prediction for object recognition. In particular we address the problems related to estimation of s...
Rong Wang, Bir Bhanu
MM
2005
ACM
123views Multimedia» more  MM 2005»
16 years 1 months ago
How speech/text alignment benefits web-based learning
This demonstration presents an integrated web-based synchronized scenario for many-to-one cross-media correlations between speech (an EFL, English as Foreign Language, lecture wit...
Sheng-Wei Li, Hao-Tung Lin, Herng-Yow Chen