We capitalize on the natural compositional structure of images in order to learn object segmentation with weakly labeled images. The intuition behind our approach is that removing objects from images will yield natural images, however removing random patches will yield unnatural images. We leverage this signal to develop a generative model that decomposes an image into layers, and when all layers are combined, it reconstructs the input image. However, when a layer is removed, the model learns to produce a different image that still looks natural to an adversary, which is possible by removing objects. Experiments and visualizations suggest that this model automatically learns object segmentation on images labeled only by scene better than baselines.
Weakly-supervised image segmentation using compositional structure of images and generative models. This paper creates a layered representation in order to better learn segmentation from unlabeled images. This paper proposes a GAN-based generative model that decomposes images into multiple layers, where the objective of the GAN is to distinguish real images from images formed by combining the layers. This paper proposes a neural network architecture around the idea of layered scene composition