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Video Instance Segm...
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Johnander, Joakim,1993-Linköpings universitet,Datorseende,Tekniska fakulteten,Zenseact, Gothenburg, Sweden
(author)
Video Instance Segmentation with Recurrent Graph Neural Networks
- Article/chapterEnglish2021
Publisher, publication year, extent ...
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2022-01-13
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Cham :Springer,2021
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printrdacarrier
Numbers
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LIBRIS-ID:oai:DiVA.org:liu-183945
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https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-183945URI
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https://doi.org/10.1007/978-3-030-92659-5_13DOI
Supplementary language notes
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Language:English
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Summary in:English
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Subject category:ref swepub-contenttype
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Subject category:kon swepub-publicationtype
Notes
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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.
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Brissman, EmilLinköpings universitet,Datorseende,Tekniska fakulteten,Saab, Linköping, Sweden(Swepub:liu)emibr12
(author)
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Danelljan, Martin,1989-Computer Vision Lab, ETH Zürich, Zürich, Switzerland(Swepub:liu)marda26
(author)
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Felsberg, Michael,1974-Linköpings universitet,Datorseende,Tekniska fakulteten,School of Engineering, University of KwaZulu-Natal, Durban, South Africa(Swepub:liu)micfe03
(author)
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Linköpings universitetDatorseende
(creator_code:org_t)
Related titles
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In:Pattern RecognitionCham : Springer, s. 206-22197830309265889783030926595
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