Scenenet rgb-d: Can 5m synthetic images beat generic imagenet pre-training on indoor segmentation? (bibtex)
by J McCormac, A Handa, S Leutenegger and AJ Davison
Reference:
Scenenet rgb-d: Can 5m synthetic images beat generic imagenet pre-training on indoor segmentation? (J McCormac, A Handa, S Leutenegger and AJ Davison), In Proceedings of the IEEE International Conference on Computer Vision, 2017. 
Bibtex Entry:
@inproceedings{mccormac2017scenenet,
 title = {Scenenet rgb-d: Can 5m synthetic images beat generic imagenet pre-training on indoor segmentation?},
 author = {J McCormac and A Handa and S Leutenegger and AJ Davison},
 booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
 pages = {2678--2687},
 year = {2017},
}
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Scenenet rgb-d: Can 5m synthetic images beat generic imagenet pre-training on indoor segmentation? (bibtex)
Scenenet rgb-d: Can 5m synthetic images beat generic imagenet pre-training on indoor segmentation? (bibtex)
by J McCormac, A Handa, S Leutenegger and AJ Davison
Reference:
Scenenet rgb-d: Can 5m synthetic images beat generic imagenet pre-training on indoor segmentation? (J McCormac, A Handa, S Leutenegger and AJ Davison), In Proceedings of the IEEE International Conference on Computer Vision, 2017. 
Bibtex Entry:
@inproceedings{mccormac2017scenenet,
 title = {Scenenet rgb-d: Can 5m synthetic images beat generic imagenet pre-training on indoor segmentation?},
 author = {J McCormac and A Handa and S Leutenegger and AJ Davison},
 booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
 pages = {2678--2687},
 year = {2017},
}
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