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CVPR
2011
IEEE
15 years 2 months ago
Learning Message-Passing Inference Machines for Structured Prediction
Nearly every structured prediction problem in computer vision requires approximate inference due to large and complex dependencies among output labels. While graphical models prov...
Stephane Ross, Daniel Munoz, J. Andrew Bagnell
CVPR
2012
IEEE
13 years 9 months ago
Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents
In this paper, a new Mixture model of Dynamic pedestrian-Agents (MDA) is proposed to learn the collective behavior patterns of pedestrians in crowded scenes. Collective behaviors ...
Bolei Zhou, Xiaogang Wang, Xiaoou Tang
CVPR
2008
IEEE
16 years 8 months ago
Learning realistic human actions from movies
The aim of this paper is to address recognition of natural human actions in diverse and realistic video settings. This challenging but important subject has mostly been ignored in...
Ivan Laptev, Marcin Marszalek, Cordelia Schmid, Be...
ICCV
2007
IEEE
16 years 8 months ago
Learning to Find Object Boundaries Using Motion Cues
While great strides have been made in detecting and localizing specific objects in natural images, the bottom-up segmentation of unknown, generic objects remains a difficult chall...
Andrew N. Stein, Derek Hoiem, Martial Hebert
ECCV
2008
Springer
16 years 8 months ago
Learning Spatial Context: Using Stuff to Find Things
The sliding window approach of detecting rigid objects (such as cars) is predicated on the belief that the object can be identified from the appearance in a small region around the...
Geremy Heitz, Daphne Koller