Self-supervised feature learning for rapid processing of marine imagery

科学研究、保护区的设计和管理以及海洋工业活动的合规性都需要对海洋环境健康进行快速可靠的定量评估。澳大利亚正在以越来越快的速度收集海底图像，但专家注释却没有跟上，这意味着典型的机器学习方法很难实现。该项目将开发自我监督技术，使用大量未标记的数据来提高性能。我们的设计利用了海洋图像的额外信息，如地理位置和遥感背景。我们将探讨这些表示如何指导额外的采样并提高分类任务的性能。

Fast and reliable quantitative estimates of marine environmental health are needed for scientific studies, design and management of protected areas, and regulatory compliance of industrial activity in the ocean. Australia is collecting seafloor images at increasing rates but expert annotations are not keeping up, meaning that typical machine learning approaches struggle. This project will develop self-supervised techniques that use large amounts of unlabeled data to enhance performance. Our design takes advantage of additional information available for marine imagery such as geolocation and remote sensing context. We will explore how these representations can guide additional sampling and improve performance in classification tasks.