先验概率
计算机科学
强化学习
人工智能
分割
机器学习
对象(语法)
注释
杠杆(统计)
可微函数
领域(数学分析)
瓶颈
贝叶斯概率
数学
数学分析
嵌入式系统
作者
Paul Hilt,Maedeh Zarvandi,Edgar Kaziakhmedov,Sourabh Bhide,Maria Laptin,Constantin Pape,Anna Kreshuk
标识
DOI:10.1109/iccvw60793.2023.00423
摘要
Instance segmentation is a fundamental 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 groundtruth 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 revoke 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 data demonstrate that a good set of priors is sufficient to reach excellent performance without any direct object-level supervision.
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