Annotation-Free and One-Shot Learning for Instance Segmentation of Homogeneous Object Clusters

We propose a novel approach for instance segmentation given an image of homogeneous object cluster (HOC). Our learning approach is one-shot because a single video of an object instance is captured and it requires no human annotation. Our intuition is that images of homogeneous objects can be effectively synthesized based on structure and illumination priors derived from real images. A novel solver is proposed that iteratively maximizes our structured likelihood to generate realistic images of HOC. Illumination transformation scheme is applied to make the real and synthetic images share the same illumination condition. Extensive experiments and comparisons are performed to verify our method. We build a dataset consisting of pixel-level annotated images of HOC. The dataset and code will be released.