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CVPR
2010
IEEE
14 years 4 months ago
What's going on? Discovering Spatio-Temporal Dependencies in Dynamic Scenes
We present two novel methods to automatically learn spatio-temporal dependencies of moving agents in complex dynamic scenes. They allow to discover temporal rules, such as the rig...
Daniel Kuettel, Michael Breitenstein, Luc Van Gool...
COGSCI
2008
75views more  COGSCI 2008»
13 years 7 months ago
Exemplars, Prototypes, Similarities, and Rules in Category Representation: An Example of Hierarchical Bayesian Analysis
This article demonstrates the potential of using hierarchical Bayesian methods to relate models and data in the cognitive sciences. This is done using a worked example that consid...
Michael D. Lee, Wolf Vanpaemel
CVPR
2009
IEEE
14 years 7 days ago
Learning to associate: HybridBoosted multi-target tracker for crowded scene
We propose a learning-based hierarchical approach of multi-target tracking from a single camera by progressively associating detection responses into longer and longer track fragm...
Yuan Li, Chang Huang, Ram Nevatia
IJCV
2008
266views more  IJCV 2008»
13 years 8 months ago
Learning to Recognize Objects with Little Supervision
This paper shows (i) improvements over state-of-the-art local feature recognition systems, (ii) how to formulate principled models for automatic local feature selection in object c...
Peter Carbonetto, Gyuri Dorkó, Cordelia Sch...
HICSS
2006
IEEE
163views Biometrics» more  HICSS 2006»
14 years 2 months ago
Learning Ranking vs. Modeling Relevance
The classical (ad hoc) document retrieval problem has been traditionally approached through ranking according to heuristically developed functions (such as tf.idf or bm25) or gene...
Dmitri Roussinov, Weiguo Fan