Abstract Video salient object detection is a challenging and important problem in computer vision domain. In recent years, deep-learning based methods have contributed to significant improvements in this domain. This paper provides an overview of recent developments in this domain and compares the corresponding methods up to date, including 1) Classification of the state-of-the-art methods and their frameworks; 2) summary of the benchmark datasets and commonly used evaluation metrics; 3) experimental comparison of the performances of the state-of-the-art methods; 4) suggestions of some promising future works for unsolved challenges.

中文翻译：

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基于深度学习的视频显着目标检测方法概述

摘要 视频显着目标检测是计算机视觉领域中一个具有挑战性和重要的问题。近年来，基于深度学习的方法为该领域的显着改进做出了贡献。本文概述了该领域的最新发展，并比较了最新的相应方法，包括 1) 最先进方法及其框架的分类；2）基准数据集和常用评估指标的总结；3) 最先进方法性能的实验比较；4）针对未解决的挑战提出一些有希望的未来工作的建议。