Annotation-Free and One-Shot Learning for Instance Segmentation of Homogeneous Object Clusters
This addresses the problem of reducing annotation effort for instance segmentation in specific domains like homogeneous objects, though it is incremental in its approach.
The paper tackles instance segmentation of homogeneous object clusters by proposing a one-shot, annotation-free method that synthesizes images using structure and illumination priors, achieving competitive results on a newly built dataset.
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.