1st Place Solution for YouTubeVOS Challenge 2022: Referring Video Object Segmentation

• URL: http://arxiv.org/abs/2212.14679v1
• Date: Tue, 27 Dec 2022 09:22:45 GMT
• Title: 1st Place Solution for YouTubeVOS Challenge 2022: Referring Video Object Segmentation
• Authors: Zhiwei Hu, Bo Chen, Yuan Gao, Zhilong Ji, Jinfeng Bai
• Abstract summary: We improve one-stage method ReferFormer to obtain mask sequences strongly correlated with language descriptions. We leverage the superior performance of video object segmentation model to further enhance the quality and temporal consistency of the mask results. Our single model reaches 70.3 J &F on the Referring Youtube-VOS validation set and 63.0 on the test set, ranking 1st place on CVPR2022 Referring Youtube-VOS challenge.
• Score: 12.100628128028385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The task of referring video object segmentation aims to segment the object in the frames of a given video to which the referring expressions refer. Previous methods adopt multi-stage approach and design complex pipelines to obtain promising results. Recently, the end-to-end method based on Transformer has proved its superiority. In this work, we draw on the advantages of the above methods to provide a simple and effective pipeline for RVOS. Firstly, We improve the state-of-the-art one-stage method ReferFormer to obtain mask sequences that are strongly correlated with language descriptions. Secondly, based on a reliable and high-quality keyframe, we leverage the superior performance of video object segmentation model to further enhance the quality and temporal consistency of the mask results. Our single model reaches 70.3 J &F on the Referring Youtube-VOS validation set and 63.0 on the test set. After ensemble, we achieve 64.1 on the final leaderboard, ranking 1st place on CVPR2022 Referring Youtube-VOS challenge. Code will be available at https://github.com/Zhiweihhh/cvpr2022-rvos-challenge.git.

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