Empirical Study of Context |
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Abstract:
This paper presents an empirical evaluation of the role of context
in a contemporary, challenging object detection task -- the
PASCAL VOC 2008. Previous experiments with context have mostly
been done on home-grown datasets, often with non-standard
baselines, making it difficult to isolate the contribution of
contextual information. In this work, we present our analysis on
a standard dataset, using top-performing local appearance
detectors as baseline. We evaluate several different sources of
context and ways to utilize it. While we employ many contextual
cues that have been used before, we also propose a few novel
ones including the use of geographic context and a new
approach for using object spatial support.
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Code
context-driven detection (zip file)
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Publications
"An Empirical Study of Context in Object Detection
Santosh K. Divvala, Derek Hoiem, James H. Hays, Alexei A. Efros, Martial Hebert
Computer Vision and Pattern Recognition (CVPR) 2009
[Paper]
[Presentation]
[Poster]
[Bibtex]
"A Unified Approach for Detection, Classification and Segmentation"
Derek Hoiem, Santosh K. Divvala, James H. Hays
European Conference on Computer Vision (ECCV) 2008, PASCAL VOC 2008 Workshop (Oral Presentation)
[Paper]
[Presentation]
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