论文标题
对抗性虚拟示例学习，用于标签 - 苏卫星图像变更检测
Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection
论文作者
论文摘要
卫星图像变化检测目的是在不同瞬间拍摄的给定场景中发现目标变化的发生。由于获取条件以及变化的主观性，此任务极具挑战性。在本文中，我们使用主动学习研究了卫星图像更改检测。我们的方法是交互式的，并且依赖于一个问答模型，该模型询问Oracle（用户）有关最有用的显示（称为虚拟示例）的问题，并且根据用户的回答，更新更新更改检测。我们方法的主要贡献是通过一种新颖的对抗模型组成，该模型允许只用最具代表性，多样化和不确定的虚拟示例来节俭探测甲骨文。学会了后者挑战最多的训练的变更决策标准，这最终导致在随后的积极学习迭代中更好地重新估计这些标准。进行的实验表明，针对其他显示策略以及相关工作，我们提出的对抗性显示模型的表现表现出色。
Satellite image change detection aims at finding occurrences of targeted changes in a given scene taken at different instants. This task is highly challenging due to the acquisition conditions and also to the subjectivity of changes. In this paper, we investigate satellite image change detection using active learning. Our method is interactive and relies on a question and answer model which asks the oracle (user) questions about the most informative display (dubbed as virtual exemplars), and according to the user's responses, updates change detections. The main contribution of our method consists in a novel adversarial model that allows frugally probing the oracle with only the most representative, diverse and uncertain virtual exemplars. The latter are learned to challenge the most the trained change decision criteria which ultimately leads to a better re-estimate of these criteria in the following iterations of active learning. Conducted experiments show the out-performance of our proposed adversarial display model against other display strategies as well as the related work.
