This technical report describes our 2nd-place solution for the ECCV 2022 YouTube-VIS Long Video Challenge. We adopt the previously proposed online video instance segmentation method IDOL for this challenge. In addition, we use pseudo labels to further help contrastive learning, so as to obtain more temporally consistent instance embedding to improve tracking performance between frames. The proposed method obtains 40.2 AP on the YouTube-VIS 2022 long video dataset and was ranked second place in this challenge. We hope our simple and effective method could benefit further research.
翻译：本技术报告描述了我们对ECCV 2022 YouTube-VIS 长视频挑战的第二位解决方案。我们采用了先前提议的在线视频实例分解方法(IDOL)来应对这一挑战。此外,我们使用假标签来进一步帮助对比性学习,以便获得更具有时间一致性的嵌入实例,从而改进各框架之间的跟踪性能。拟议方法在YouTube-VIS 2022长视频数据集上获得了40.2份AP,在这项挑战中位居第二。我们希望我们简单有效的方法能够有益于进一步的研究。
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