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Video Instance Segm...
Video Instance Segmentation with Recurrent Graph Neural Networks
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- Johnander, Joakim, 1993- (författare)
- Linköpings universitet,Datorseende,Tekniska fakulteten,Zenseact, Gothenburg, Sweden
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- Brissman, Emil (författare)
- Linköpings universitet,Datorseende,Tekniska fakulteten,Saab, Linköping, Sweden
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- Danelljan, Martin, 1989- (författare)
- Computer Vision Lab, ETH Zürich, Zürich, Switzerland
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- Felsberg, Michael, 1974- (författare)
- Linköpings universitet,Datorseende,Tekniska fakulteten,School of Engineering, University of KwaZulu-Natal, Durban, South Africa
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(creator_code:org_t)
- 2022-01-13
- 2021
- Engelska.
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Ingår i: Pattern Recognition. - Cham : Springer. - 9783030926588 - 9783030926595 ; , s. 206-221
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Video instance segmentation is one of the core problems in computer vision. Formulating a purely learning-based method, which models the generic track management required to solve the video instance segmentation task, is a highly challenging problem. In this work, we propose a novel learning framework where the entire video instance segmentation problem is modeled jointly. To this end, we design a graph neural network that in each frame jointly processes all detections and a memory of previously seen tracks. Past information is considered and processed via a recurrent connection. We demonstrate the effectiveness of the proposed approach in comprehensive experiments. Our approach, operating at over 25 FPS, outperforms previous video real-time methods. We further conduct detailed ablative experiments that validate the different aspects of our approach.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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