Stateless actor-critic for instance segmentation with high-level priors

TL;DR

This paper introduces a novel reinforcement learning approach using stateless actor critic methods to perform instance segmentation guided by high-level priors, reducing the need for extensive annotated data.

Contribution

It presents a new framework that incorporates non-differentiable high-level priors into instance segmentation via reinforcement learning, bypassing the need for detailed annotations.

Findings

Achieves high-quality segmentation without direct supervision.

Effectively incorporates high-level priors like shape, position, and size.

Demonstrates success on toy and real datasets.

Abstract

Instance segmentation is an important computer vision problem which remains challenging despite impressive recent advances due to deep learning-based methods. Given sufficient training data, fully supervised methods can yield excellent performance, but annotation of ground-truth data remains a major bottleneck, especially for biomedical applications where it has to be performed by domain experts. The amount of labels required can be drastically reduced by using rules derived from prior knowledge to guide the segmentation. However, these rules are in general not differentiable and thus cannot be used with existing methods. Here, we relax this requirement by using stateless actor critic reinforcement learning, which enables non-differentiable rewards. We formulate the instance segmentation problem as graph partitioning and the actor critic predicts the edge weights driven by the rewards, which are based on the conformity of segmented instances to high-level priors on object shape, position or size. The experiments on toy and real datasets demonstrate that we can achieve excellent performance without any direct supervision based only on a rich set of priors.