Multi-Scale Context Aggregation By Dilated Convolutions
The semantic segmentation task in computer vision involves partitioning an image into a set of multiple non-overlapping and semantically interpretable regions. This entails assigning pixel-wise class labels to the entire image, making it a dense prediction task. Owing to the massive improvements in image classification performance achieved by CNNs over the recent years, there have been several works which successfully repurpose these popular image classification CNN architectures for dense prediction tasks. This paper questions this approach, and instead investigates if modules specifically designed for a dense prediction task would improve the segmentation performance even further. Unlike image classification networks which aggregate multi-scale contextual information through successive downsampling operations to obtain a global prediction, a dense prediction task like semantic segmentation requires “multi-scale contextual reasoning in combination with full-resolution output”. % However, increasing the receptive field of the convolution operator comes at the cost of more parameters. The authors therefore propose using the dilated convolution operator to address this. To this end, this paper makes threefold contributions: (a) a generalized form of the convolution operator to account for dilation, (b) a multi-scale context aggregation module that relies on dilated convolution, and (c) a simiplified front-end module which gets rid of “vestigial components” carried over from image classification networks. ...