State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction problems such as semantic segmentation are structurally different from image classification. In this work, we develop a new convolutional network module that is specifically designed for dense prediction. The presented module uses dilated convolutions to systematically aggregate multiscale contextual information without losing resolution. The architecture is based on the fact that dilated convolutions support exponential expansion of the receptive field without loss of resolution or coverage. We show that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems. In addition, we examine the adaptation of image classification networks to dense prediction and show that simplifying the adapted network can increase accuracy.
The cutting-edge method, namely, DilatedNet, achieved state-of-the-art performance on semantic segmentation by introducing atrous convolution, which extends the size of the receptive field without loss of resolution REF .
Multi-Scale Context Aggregation by Dilated Convolutions
ICLR 2016
arXiv: Computer Vision and Pattern Recognition
Computer Science