The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Feedforward semantic segmentation with zoom-out features
Abstract
We introduce a purely feed-forward architecture for semantic segmentation. We map small image elements (superpixels) to rich feature representations extracted from a sequence of nested regions of increasing extent. These regions are obtained by “zooming out” from the superpixel all the way to scene-level resolution. This approach exploits statistical structure in the image and in the label space without setting up explicit structured prediction mechanisms, and thus avoids complex and expensive inference. Instead superpixels are classified by a feedforward multilayer network. Our architecture achieves 69.6% average accuracy on the PASCAL VOC 2012 test set.
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Citations
Cites
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Cited by
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“The usage of larger FOVs to capture more image spatial context is inspired by deep zoom-out features [44] that improve semantic segmentation.”
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Metadata
- Authors
- Mostajabi, M., Yadollahpour, P., & Shakhnarovich, G.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 3376-3385
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1938976761
- Cited by
- 436
- References
- 7
- FWCI
- 31.31
- Type
- conference paper
- Language
- en
- Open access
- closed
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