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Video object segmentation (VOS) has made significant progress with the riseof 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 proposea simple yet effective solution for this task. In the solution, we firstanalyze the distribution of the Youtube-VOS dataset and supplement the datasetby introducing public static and video segmentation datasets. Then, we improvethree network architectures with different characteristics and train severalnetworks to learn the different characteristics of objects in videos. Afterthat, we use a simple way to integrate all results to ensure that differentmodels complement each other. Finally, subtle post-processing is carried out toensure accurate video object segmentation with precise boundaries. Extensiveexperiments on Youtube-VOS dataset show that the proposed solution achieves thestate-of-the-art performance with an 86.1% overall score on the YouTube-VOS2022 test set, which is 5th place on the video object segmentation track of theYoutube-VOS Challenge 2022.

5th Place Solution for YouTube-VOS Challenge 2022: Video Object Segmentation