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Conference Paper: FASSST: Fast attention based single-stage segmentation net for real-time instance segmentation

TitleFASSST: Fast attention based single-stage segmentation net for real-time instance segmentation
Authors
Cheng, YLIN, RZhen, PHou, TNg, CWChen, HBYu, HWong, N
Issue Date2022
Citation
Winter Conference on Applications of Computer Vision (WACV) 2022, Online Conference, Waikoloa, HI, 5-9 January 2022 How to Cite?
Description6D (virtual): Segmentation, Tracking, and Scene Understanding
Persistent Identifierhttp://hdl.handle.net/10722/307963

 

DC FieldValueLanguage
dc.contributor.authorCheng, Y-
dc.contributor.authorLIN, R-
dc.contributor.authorZhen, P-
dc.contributor.authorHou, T-
dc.contributor.authorNg, CW-
dc.contributor.authorChen, HB-
dc.contributor.authorYu, H-
dc.contributor.authorWong, N-
dc.date.accessioned2021-11-12T13:40:28Z-
dc.date.available2021-11-12T13:40:28Z-
dc.date.issued2022-
dc.identifier.citationWinter Conference on Applications of Computer Vision (WACV) 2022, Online Conference, Waikoloa, HI, 5-9 January 2022-
dc.identifier.urihttp://hdl.handle.net/10722/307963-
dc.description6D (virtual): Segmentation, Tracking, and Scene Understanding-
dc.languageeng-
dc.relation.ispartofWinter Conference on Applications of Computer Vision (WACV)-
dc.titleFASSST: Fast attention based single-stage segmentation net for real-time instance segmentation-
dc.typeConference_Paper-
dc.identifier.emailWong, N: nwong@eee.hku.hk-
dc.identifier.authorityWong, N=rp00190-
dc.identifier.hkuros329306-

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