Residual Conv-Deconv Grid Network for Semantic Segmentation

Damien Fourure, Rémi Emonet, Elisa Fromont, Damien Muselet, Alain Tremeau and Christian Wolf

Abstract

This paper presents GridNet, a new Convolutional Neural Network (CNN) architecture for semantic image segmentation (full scene labelling). Classical neural networks are implemented as one stream from the input to the output with subsampling operators applied in the stream in order to reduce the feature maps size and to increase the receptive field for the final prediction. However, for semantic image segmentation, where the task consists in providing a semantic class to each pixel of an image, feature maps reduction is harmful because it leads to a resolution loss in the output prediction. To tackle this problem, our GridNet follows a grid pattern allowing multiple interconnected streams to work at different resolutions. We show that our network generalizes many well known networks such as conv-deconv, residual or U-Net networks.

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DOI

10.5244/C.31.181
https://dx.doi.org/10.5244/C.31.181

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Bibtex

            @inproceedings{BMVC2017_181,
                title={Residual Conv-Deconv Grid Network for Semantic Segmentation},
                author={Damien Fourure, Rémi Emonet, Elisa Fromont, Damien Muselet, Alain Tremeau and Christian Wolf},
                year={2017},
                month={September},
                pages={181.1-181.13},
                articleno={181},
                numpages={13},
                booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
                publisher={BMVA Press},
                editor={Tae-Kyun Kim, Stefanos Zafeiriou, Gabriel Brostow and Krystian Mikolajczyk},
                doi={10.5244/C.31.181},
                isbn={1-901725-60-X},
                url={https://dx.doi.org/10.5244/C.31.181}
            }