Dense Convolution for Semantic Segmentation

Abstract

State-of-the-art semantic segmentation methods adopt fully convolutional neural networks (FCNs) to solve this dense prediction problem. However, replacing fully connected layers with the standard 2D convolution layer is straightforward yet not optimal in generating segmentation results. In this paper we develop a dense convolution scheme that is more suitable for semantic segmentation. Instead of generating a single output, dense convolution produces the same number of output as its input and introduces spatial overlaps into current convolutions. Then each activation is obtained from multiple overlapped dense convolutions with learnable weights. Such dense convolution helps to reinforce local connections between activations and provide more flexible receptive fields for predictions. Experiments on benchmark dataset demonstrate the effectiveness of the proposed approach in semantic segmentation tasks.