TrSeg: Transformer for semantic segmentation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jin, Youngsaeng | - |
dc.contributor.author | Han, David | - |
dc.contributor.author | Ko, Hanseok | - |
dc.date.accessioned | 2022-02-25T18:41:08Z | - |
dc.date.available | 2022-02-25T18:41:08Z | - |
dc.date.created | 2022-02-09 | - |
dc.date.issued | 2021-08 | - |
dc.identifier.issn | 0167-8655 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/136892 | - |
dc.description.abstract | Recent effort s in semantic segment ation using deep learning frameworks have made notable advances. However, capturing the existence of objects in an image at multiple scales still remains a challenge. In this paper, we address the semantic segmentation task based on transformer architecture. Unlike exist-ing methods that capture multi-scale contextual information through infusing every single-scale piece of information from parallel paths, we propose a novel semantic segmentation network incorporating a transformer (TrSeg) to adaptively capture multi-scale information with the dependencies on original con-textual information. Given the original contextual information as keys and values, the multi-scale con-textual information from the multi-scale pooling module as queries is transformed by the transformer decoder. The experimental results show that TrSeg outperforms the other methods of capturing multi-scale information by large margins. (c) 2021 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.title | TrSeg: Transformer for semantic segmentation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jin, Youngsaeng | - |
dc.contributor.affiliatedAuthor | Ko, Hanseok | - |
dc.identifier.wosid | 000674680600005 | - |
dc.identifier.bibliographicCitation | PATTERN RECOGNITION LETTERS, v.148, pp.29 - 35 | - |
dc.relation.isPartOf | PATTERN RECOGNITION LETTERS | - |
dc.citation.title | PATTERN RECOGNITION LETTERS | - |
dc.citation.volume | 148 | - |
dc.citation.startPage | 29 | - |
dc.citation.endPage | 35 | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
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