The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation

Abstract

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

River segmentation for flood monitoring

“…Moreover, in this architectures upsampling layers were also introduced to obtain an output of the same resolution of the input and skip layers, which combine finer information from earlier layers with semantically more relevant information from deeper layers, making the whole algorithm trainable endto-end. Given the advantages of FCN we have chosen two stat-of-the-art algorithms [10], [11] based on this technique which have reported top performances in well known segmentation datasets such as PASCAL [15] and CamVid [16]. Additionally, we compare the performances obtained with a segmentation algorithm based on Conditional Adversial Networks [12], which has shown very good results in several computer vision tasks, including semantic segmentation.…”

“…As a general rule, deeper networks extract higher semantic information but at the same time they lose pixel location information. Several techniques have been studied for solving this drawback, such as bilinear interpolation [8], unpoooling operations [9], [8] or skip-layers, which combine fine information from early layers with coarse information from deeper layers [10], [11]. Algorithms based on Adversarial Networks have also been applied to segmentation problems [12], [13].…”

“…This algorithms were not trainable end-to-end and could only work with images of a fixed input size due to the presence of fully connected layers operating with a fixed sized input. Later, Fully Convolutional Networks (FCN) were introduced to solve the problem of semantic segmentation [10], [14], [11]. Using a fully convolutional network allows to process input images of variable size.…”

“…In this paper, we train three different stateof-the-art algorithms [10], [11], [12] on a novel publicly available dataset of river images, which we introduce in Section IV.…”

“…Skip layers are added to combine the prediction layer with previous layers with finer strides 1 . 2) Fully Convolutional DenseNets for Semantic Segmentation (Tiramisu) [11]: Similarly to FCN8-s, in Tiramisu a CNN network proposed for image classification is adapted into a FCN network with skip and upsampling layers to perform semantic segmentation. However, Tiramisu is based on an CNN architecture called DenseNet which was first introduced in [20].…”