MVTSII Quick Start Guide
Iccv2009 recognition and learning object categories p2 c02 - recognizing muliple objects in an image - sharing and context
Brain reading, compressive sensing, fMRI and statistical learning in Python
Chalkboard Challenge Mrs. LaRue StudentsTeachers Game Board Metric Length Metric Mass Metric Volume Density Comparisons 100 200 300 400 500 Lets Play.
CREATIVE SYNTHESIS David Pearson Room T10, William Guild Building [email protected].
Context-based object-class recognition and retrieval by generalized correlograms by J. Amores, N. Sebe and P. Radeva Discussion led by Qi An Duke University.
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Article review by Alexander Backus Distributed representations meeting article review.
Intro CALTECH 256 Greg Griffin, Alex Holub and Pietro Perona.
Robust Subspace Discovery: Low-rank and Max-margin Approaches Xiang Bai Joint works with Xinggang Wang, Zhengdong Zhang, Zhuowen Tu, Yi Ma and Wenyu Liu.
Problem: SVM training is expensive – Mining for hard negatives, bootstrapping Solution: LDA (Linear Discriminant Analysis). – Extremely fast training,
How many object categories are there? Biederman 1987.