GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation

Abstract

Segmenting an image into its parts is a common preprocess for high-level vision tasks such as image editing. However, annotating masks for supervised training is expensive. Weakly-supervised and unsupervised methods exist, but they depend on the comparison of pairs of images, such as from multi-views, frames of videos, and image transformations of single images, which limit their applicability. To address this, we propose a GAN-based approach that generates images conditioned on latent masks, thereby alleviating full or weak annotations required by previous approaches. We show that such mask-conditioned image generation can be learned faithfully when conditioning the masks in a hierarchical manner on 2D latent points that define the position of parts explicitly. Without requiring supervision of masks or points, this strategy increases robustness of mask to viewpoint and object position changes. It also lets us generate image-mask pairs for training a segmentation network, which outperforms state-of-the-art unsupervised segmentation methods on established benchmarks.