Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Rong-Cheng Tu"'
Autor:
Rong-Cheng Tu, 涂榮城
105
This thesis investigates a new multi-select museum routing problem (MS-MVRP) in which a number of groups visit several exhibition rooms in a museum simultaneously. The exhibition rooms are divided into two categories: (a) a set of must-see e
This thesis investigates a new multi-select museum routing problem (MS-MVRP) in which a number of groups visit several exhibition rooms in a museum simultaneously. The exhibition rooms are divided into two categories: (a) a set of must-see e
Externí odkaz:
http://ndltd.ncl.edu.tw/handle/7rx66q
Publikováno v:
IEEE Transactions on Multimedia. :1-12
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7855e1f01897dc48294073527034d2e9
https://doi.org/10.1109/tmm.2023.3243608
https://doi.org/10.1109/tmm.2023.3243608
Autor:
Rong-Cheng Tu, Xian-Ling Mao, Rong-Xin Tu, Binbin Bian, Chengfei Cai, Hongfa wang, Wei Wei, Heyan Huang
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. :1-13
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::977568ee77e4a95a5eeab0d6ce2fcb82
https://doi.org/10.1109/tkde.2022.3187023
https://doi.org/10.1109/tkde.2022.3187023
Publikováno v:
IEEE Transactions on Circuits and Systems for Video Technology. :1-1
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4aae799ac8a0eb131b484f75f87fbd98
https://doi.org/10.1109/tcsvt.2023.3251395
https://doi.org/10.1109/tcsvt.2023.3251395
Publikováno v:
ACM Multimedia
Recently, deep Hamming hashing methods have been proposed for Hamming space retrieval which enables constant-time search by hash table lookups instead of linear scan. When carrying out Hamming space retrieval, for each query datapoint, there is a Ham
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bba8b582d545e56962683223eab43653
https://doi.org/10.1145/3474085.3475498
https://doi.org/10.1145/3474085.3475498
Publikováno v:
WWW
Recently, deep supervised hashing methods have shown state-of-the-art performance by integrating feature learning and hash codes learning into an end-to-end network to generate high-quality hash codes. However, it is still a challenge to learn discri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::376fe87b3c9058cfcf0165d3da1d2e94
https://doi.org/10.1145/3442381.3449825
https://doi.org/10.1145/3442381.3449825
Publikováno v:
ACL/IJCNLP (Findings)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f968b157b32ad26dd06f94ecc110c143
https://doi.org/10.18653/v1/2021.findings-acl.66
https://doi.org/10.18653/v1/2021.findings-acl.66
Publikováno v:
IJCAI
Most of the unsupervised hashing methods usually map images into semantic similarity-preserving hash codes by constructing local semantic similarity structure as guiding information, i.e., treating each point similar to its k nearest neighbours. Howe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a077533d8afed3645978d0948b0b207d
https://doi.org/10.24963/ijcai.2020/479
https://doi.org/10.24963/ijcai.2020/479
Publikováno v:
IJCAI
Recently, similarity-preserving hashing methods have been extensively studied for large-scale image retrieval. Compared with unsupervised hashing, supervised hashing methods for labeled data have usually better performance by utilizing semantic label
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd58cc0040794d60359bdcc2d5bcb4f8
https://doi.org/10.24963/ijcai.2019/500
https://doi.org/10.24963/ijcai.2019/500
Due to their high retrieval efficiency and low storage cost, cross-modal hashing methods have attracted considerable attention. Generally, compared with shallow cross-modal hashing methods, deep cross-modal hashing methods can achieve a more satisfac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::98921e8f6d78f6d74b77ab666e84b292
http://arxiv.org/abs/1907.12490
http://arxiv.org/abs/1907.12490