https://scholars.lib.ntu.edu.tw/handle/123456789/581094
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Chen T.-S | en_US |
dc.contributor.author | Liu C.-T | en_US |
dc.contributor.author | Wu C.-W | en_US |
dc.contributor.author | Chien S.-Y. | en_US |
dc.contributor.author | SHAO-YI CHIEN | zz |
dc.creator | Chen T.-S;Liu C.-T;Wu C.-W;Chien S.-Y. | - |
dc.date.accessioned | 2021-09-02T00:07:18Z | - |
dc.date.available | 2021-09-02T00:07:18Z | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 03029743 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097249109&doi=10.1007%2f978-3-030-58536-5_20&partnerID=40&md5=08d42a9c48dd0666cc91773788dafb51 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/581094 | - |
dc.description.abstract | Vehicle re-identification (re-ID) focuses on matching images of the same vehicle across different cameras. It is fundamentally challenging because differences between vehicles are sometimes subtle. While several studies incorporate spatial-attention mechanisms to help vehicle re-ID, they often require expensive keypoint labels or suffer from noisy attention mask if not trained with expensive labels. In this work, we propose a dedicated Semantics-guided Part Attention Network (SPAN) to robustly predict part attention masks for different views of vehicles given only image-level semantic labels during training. With the help of part attention masks, we can extract discriminative features in each part separately. Then we introduce Co-occurrence Part-attentive Distance Metric (CPDM) which places greater emphasis on co-occurrence vehicle parts when evaluating the feature distance of two images. Extensive experiments validate the effectiveness of the proposed method and show that our framework outperforms the state-of-the-art approaches. ? 2020, Springer Nature Switzerland AG. | - |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.subject | Computer vision; Semantic Web; Semantics; Co-occurrence; Discriminative features; Distance metrics; Feature distance; Re identifications; Semantic labels; Spatial attention; State-of-the-art approach; Vehicles | - |
dc.title | Orientation-Aware Vehicle Re-Identification with Semantics-Guided Part Attention Network | en_US |
dc.type | conference paper | en |
dc.identifier.doi | 10.1007/978-3-030-58536-5_20 | - |
dc.identifier.scopus | 2-s2.0-85097249109 | - |
dc.relation.pages | 330-346 | - |
dc.relation.journalvolume | 12347 LNCS | - |
item.fulltext | no fulltext | - |
item.grantfulltext | none | - |
item.openairetype | conference paper | - |
item.openairecristype | http://purl.org/coar/resource_type/c_5794 | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | Electronics Engineering | - |
crisitem.author.dept | Electrical Engineering | - |
crisitem.author.dept | Intel-NTU Connected Context Computing Center | - |
crisitem.author.dept | Networking and Multimedia | - |
crisitem.author.dept | MediaTek-NTU Research Center | - |
crisitem.author.orcid | 0000-0002-0634-6294 | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
crisitem.author.parentorg | Others: International Research Centers | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
顯示於: | 電機工程學系 |
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