March on Data Imperfections: Domain Division and Domain Generalization for Semantic Segmentation

Abstract

Significant progress has been made in semantic segmentation by deep neural networks, most of which concentrate on discriminative representation learning. However, model performances suffer from deterioration when the training process is optimized without awareness of data imperfections (e.g., data imbalance and label noise). In contrast to previous works, we present a novel model-agnostic training optimization algorithm which has two prominent components: Domain Division and Domain Generalization. Rather than sampling all pixels uniformly, an uncertainty-based Domain Division method is proposed to deal with data imbalance, which dynamically decomposes the pixels into meta-train and meta-test domains according to whether they lie near the classification boundary. The meta-train domain corresponds to highly-uncertain but more informative pixels and determines the current main update direction. Furthermore, to alleviate the degradation caused by label noise, we propose a Domain Generalization technique with a meta-optimization objective which ensures that update on the meta-train domain should generalize to the meta-test domain. Comprehensive experimental results on three public benchmarks across multi-modalities show that the proposed optimization algorithm is superior to other segmentation optimization methods and significantly outperforms conventional methods without introducing additional model parameters.