Zobrazeno 1 - 10
of 145
pro vyhledávání: '"Wang, Shengke"'
Publikováno v:
In Pattern Recognition November 2024 155
Publikováno v:
Nanotechnology Reviews, Vol 12, Iss 1, Pp 134-42 (2023)
Externí odkaz:
https://doaj.org/article/f9b4429697244107a88bf066f5484a64
Autor:
Chen, Long, Zhou, Feixiang, Wang, Shengke, Dong, Junyu, Li, Ning, Ma, Haiping, Wang, Xin, Zhou, Huiyu
In recent years, deep learning based object detection methods have achieved promising performance in controlled environments. However, these methods lack sufficient capabilities to handle underwater object detection due to these challenges: (1) image
Externí odkaz:
http://arxiv.org/abs/2010.10006
In recent years, deep learning based methods have achieved promising performance in standard object detection. However, these methods lack sufficient capabilities to handle underwater object detection due to these challenges: (1) Objects in real appl
Externí odkaz:
http://arxiv.org/abs/2005.11552
Autor:
Yao, Fengqin, Wang, Shengke, Ding, Laihui, Zhong, Guoqiang, Bullock, Leon Bevan, Xu, Zhiwei, Dong, Junyu
Publikováno v:
In Knowledge-Based Systems 25 January 2023 260
Autor:
Chen, Long, Zhou, Feixiang, Wang, Shengke, Dong, Junyu, Li, Ning, Ma, Haiping, Wang, Xin, Zhou, Huiyu
Publikováno v:
In Pattern Recognition December 2022 132
Publikováno v:
In Neurocomputing 14 April 2022 482:264-277
Fine-grained recognition is a challenging task due to the small intra-category variances. Most of top-performing fine-grained recognition methods leverage parts of objects for better performance. Therefore, part annotations which are extremely comput
Externí odkaz:
http://arxiv.org/abs/1702.08692
Akademický článek
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Publikováno v:
In Journal of Visual Communication and Image Representation February 2019 59:215-230