Semantic Segmentation Based on Multiple Granularity Learning

Abstract

Accurate and robust coarse semantic segmentation plays a key role in the pursuit of autonomous driving. We present an algorithm that regularizes the representation space of Semantic Segmentation by Multiple Granularity Learning (SSMGL). This approach explores multiple levels of semantic knowledge in an unified framework, where the fine-grained semantic information can be either labeled or unlabeled. In our experiments, we find that SSMGL can achieve better results (1) on both on-road and off-road benchmarks, (2) under different segmentation architectures, or (3) with different backbones. The method is plug-and-play, not specialized for autonomous driving applications, and can be easily extended to any other segmentation scenario. Moreover, our SSMGL approach does not increase the computational overhead in the inference stage.