https://scholars.lib.ntu.edu.tw/handle/123456789/580962
標題: | Comprehensive Detail Refinement Network for Vehicle Re-identification | 作者: | Wu C.-W JIAN-JIUN DING |
關鍵字: | Engineering; Industrial engineering; Benchmark datasets; Diverse features; Local feature; Local refinement; Re identifications; State-of-the-art approach; Vehicle images; Vehicles | 公開日期: | 2020 | 起(迄)頁: | 199-202 | 來源出版物: | 2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020 | 摘要: | A novel comprehensive detail refinement network, called the CDRNet, to learn robust and diverse features from vehicle images is proposed. There are three modules in the proposed algorithm: the global attention, the detail, and the local feature refinement modules. The global attention module extracts crucial global characteristics while the detail and local refinement modules retrieve important minor features. Experiments on benchmark datasets, VeRi-776 and VehicleID, show that the proposed network outperforms state-of-the-art approaches and is very helpful for vehicle re-identification. ? 2020 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099582922&doi=10.1109%2fECICE50847.2020.9301953&partnerID=40&md5=39ec04bc32854833adc85300c3b142e1 https://scholars.lib.ntu.edu.tw/handle/123456789/580962 |
DOI: | 10.1109/ECICE50847.2020.9301953 |
顯示於: | 電機工程學系 |
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