End-to-End Video Instance Segmentation With Transformers
Yuqing Wang, Zhaoliang Xu, Xinlong Wang, Chunhua Shen, Baoshan Cheng, Hao Shen, Huaxia Xia
2021Year
198Top-tier citations
Abstract
speed among all existing VIS models, and achieves the best result among methods using single model on the YouTube-VIS dataset. For the first time, we demonstrate a much simpler and faster video instance segmentation framework built upon Transformers, achieving competitive accuracy. We hope that VisTR can motivate future research for more video understanding tasks.
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Install the CLIlune papers fulltext d809fedb-d77b-40d8-8146-eabd58beaa73Cited by top-tier papers198
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Builds on5
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- Video Instance SegmentationLinjie Yang, Yuchen Fan, Ning XuICCV 2019 · 615 citations
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- Classifying, Segmenting, and Tracking Object Instances in Video with Mask PropagationGedas Bertasius, Lorenzo TorresaniCVPR 2020
- BlendMask: Top-Down Meets Bottom-Up for Instance SegmentationHao Chen, Kunyang Sun, Zhi Tian, Chunhua Shen et al.CVPR 2020
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