Video object segmentation (VOS) has made significant progress with the rise of deep learning. However, there still exist some thorny problems, for example, similar objects are easily confused and tiny objects are difficult to be found. To solve these problems and further improve the performance of VOS, we propose a simple yet effective solution for this task. In the solution, we first analyze the distribution of the Youtube-VOS dataset and supplement the dataset by introducing public static and video segmentation datasets. Then, we improve three network architectures with different characteristics and train several networks to learn the different characteristics of objects in videos. After that, we use a simple way to integrate all results to ensure that different models complement each other. Finally, subtle post-processing is carried out to ensure accurate video object segmentation with precise boundaries. Extensive experiments on Youtube-VOS dataset show that the proposed solution achieves the state-of-the-art performance with an 86.1% overall score on the YouTube-VOS 2022 test set, which is 5th place on the video object segmentation track of the Youtube-VOS Challenge 2022.

本文提出了一个简单而有效的解决方案，通过分析数据集分布、引入公共静态和视频分割数据集增补数据集，改进了三种不同特性的网络结构并训练多个网络去学习视频中物体的不同特性，通过简单的整合和精细的后处理确保精准的视频对象分割。在Youtube-VOS数据集上的广泛实验表明，该解决方案达到了86.1%的整体分数，是Youtube-VOS Challenge 2022中视频对象分割轨迹第五名的最新技术水平。

YouTube-VOS 2022大赛第5名解决方案：视频目标分割