  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 and image classification are
structurally different. 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 multi-scale 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.
