Subdivided Mask Dispersion Framework for semi-supervised semantic segmentation

Abstract

Learning the relationship between weak and strong perturbations has been considered a major part of semi-supervised semantic segmentation. We observed two problems with a publicly used perturbation method, which randomly generates a mask with a single large bounding box. The large single bounding box that entirely covers the important object components in an image, hindering the model from capturing partial object information. Furthermore, training the model with a single large bounding box as an image-level perturbation causes the model to be biased towards the shape of the large squared box, rather than the deformable object component shapes. In this paper, we propose Subdivided Mask Dispersion Framework (SMDF) to solve these problems. Our framework disperses the large squared box into small multi-scale boxes, capturing the crucial multi-scaled object components in the image. SMDF achieves state-of-the-art performance on five data partitions of PASCAL dataset and three partitions of Extended SBD dataset. Our extensive ablation studies show the effectiveness of dispersed small multi-scale bounding boxes in semi-supervised semantic segmentation.