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Modeling temporal information for both detection and tracking in a unifiedframework has been proved a promising solution to video instance segmentation(VIS). However, how to effectively incorporate the temporal information into anonline model remains an open problem. In this work, we propose a new online VISparadigm named Instance As Identity (IAI), which models temporal informationfor both detection and tracking in an efficient way. In detail, IAI employs anovel identification module to predict identification number for trackinginstances explicitly. For passing temporal information cross frame, IAIutilizes an association module which combines current features and pastembeddings. Notably, IAI can be integrated with different image models. Weconduct extensive experiments on three VIS benchmarks. IAI outperforms all theonline competitors on YouTube-VIS-2019 (ResNet-101 43.7 mAP) andYouTube-VIS-2021 (ResNet-50 38.0 mAP). Surprisingly, on the more challengingOVIS, IAI achieves SOTA performance (20.6 mAP). Code is available athttps://github.com/zfonemore/IAI

Instance As Identity: A Generic Online Paradigm for Video Instance Segmentation