mlsanityData to 7 Oct 2026

Tracking Instances as Queries

arXiv 2106.11963 ↗First posted 22 Jun 2021cs.CV cs.AI cs.MMcited by 7

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Abstract (arXiv)

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 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 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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 23 of its 24 references that are in the map. First posted on arXiv 22 Jun 2021.

Builds on

23 of 24 references.

References · 23

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11 more
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Citing papers per year: 2022: 4; 2023: 1; 2024: 2.

7 papers.