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. Code to reproduce the experiments is publicly available here : https://github.com/SimJeg/FC-DenseNet
In the semantic segmentation task, Tiramisu REF used the connection of the dense block in up-sampling and down-sampling to improve U-Net and obtained state-of-the-art results in the urban scene benchmark datasets.
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Computer Science