The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
Open accessAbstract
State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions. Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train. In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets.
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Cited by
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2017) · arXiv · doi:10.48550/arxiv.1710.10903 · Cited by 12,107
Self-citationnnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
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What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Kendall, A., & Gal, Y. (2017) · arXiv · doi:10.48550/arxiv.1703.04977 · Cited by 3,088
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
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DenseASPP for Semantic Segmentation in Street Scenes
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Metadata
- Authors
- Jegou, S., Drozdzal, M., Vazquez, D., Romero, A., & Bengio, Y.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 1175-1183
- OpenAlex
- W2559597482
- Cited by
- 1,736
- References
- 20
- FWCI
- 64.55
- Type
- conference paper
- Language
- en
- Open access
- green
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- Jegou, S., Drozdzal, M., Vazquez, D., Romero, A., & Bengio, Y. (2017). The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/cvprw.2017.156
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