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• W3176180954 abstract "Recently, query based deep networks catch lots of attention owing to their end-to-end pipeline and competitive results on several fundamental computer vision tasks, such as object detection, semantic segmentation, and instance segmentation. However, how to establish a query based video instance segmentation (VIS) framework with elegant architecture and strong performance remains to be settled. In this paper, we present textbf{QueryTrack} (i.e., tracking instances as queries), a unified query based VIS framework fully leveraging the intrinsic one-to-one correspondence between instances and queries in QueryInst. The proposed method obtains 52.7 / 52.3 AP on YouTube-VIS-2019 / 2021 datasets, which wins the 2-nd place in the YouTube-VIS Challenge at CVPR 2021 textbf{with a single online end-to-end model, single scale testing & modest amount of training data}. We also provide QueryTrack-ResNet-50 baseline results on YouTube-VIS-2021 val set as references for the VIS community." @default.
• W3176180954 created "2021-07-05" @default.
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• W3176180954 date "2021-06-22" @default.
• W3176180954 modified "2023-09-26" @default.
• W3176180954 title "Tracking Instances as Queries" @default.
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• W3176180954 doi "https://doi.org/10.48550/arxiv.2106.11963" @default.
• W3176180954 hasPublicationYear "2021" @default.
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