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Semantic contours from inverse detectors
Abstract
We study the challenging problem of localizing and classifying category-specific object contours in real world images. For this purpose, we present a simple yet effective method for combining generic object detectors with bottom-up contours to identify object contours. We also provide a principled way of combining information from different part detectors and across categories. In order to study the problem and evaluate quantitatively our approach, we present a dataset of semantic exterior boundaries on more than 20, 000 object instances belonging to 20 categories, using the images from the VOC2011 PASCAL challenge [7].
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Cites
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Metadata
- Authors
- Hariharan, B., Arbelaez, P., Bourdev, L., Maji, S., & Malik, J.
- Venue
- 2011 International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2011-11
- Pages
- 991-998
- OpenAlex
- W2144794286
- Cited by
- 1,624
- References
- 2
- FWCI
- 8.37
- Type
- conference paper
- Language
- en
- Open access
- closed
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