We propose a novel approach for instance segmen- tation given an image of
homogeneous object clus- ter (HOC). Our learning approach is one-shot be- cause
a single video of an object instance is cap- tured and it requires no human
annotation. Our in- tuition 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 im- ages 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 ver- ify our method. We build a dataset consisting of pixel-level
annotated images of HOC. The dataset and code will be published with the paper.

本文提出了一种单镜头拍摄无需人工标注的全新方法，包括学习方法、新型的求解器、光照变换方案，并建立了一个由像素级注释的同质对象簇数据集，对这一方法进行了广泛的实验和比较。