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We propose a novel approach for instance segmen- tation given an image ofhomogeneous object clus- ter (HOC). Our learning approach is one-shot be- causea single video of an object instance is cap- tured and it requires no humanannotation. Our in- tuition is that images of homogeneous objects can beeffectively synthesized based on structure and illumination priors derived fromreal images. A novel solver is proposed that iteratively maximizes ourstructured likelihood to generate realistic im- ages of HOC. Illuminationtransformation scheme is applied to make the real and synthetic images sharethe same illumination condition. Extensive experiments and comparisons areperformed to ver- ify our method. We build a dataset consisting of pixel-levelannotated images of HOC. The dataset and code will be published with the paper.

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