Zobrazeno 1 - 10
of 354
pro vyhledávání: '"Zhang Yongfei"'
Publikováno v:
Journal of Engineering Science and Technology Review, Vol 11, Iss 5, Pp 35-43 (2018)
Externí odkaz:
https://doaj.org/article/d579282240854502a8a3c92868d72038
Autor:
Yang, Shan, Zhang, Yongfei
Multimodal large language models (MLLM) have achieved satisfactory results in many tasks. However, their performance in the task of ReID (ReID) has not been explored to date. This paper will investigate how to adapt them for the task of ReID. An intu
Externí odkaz:
http://arxiv.org/abs/2401.13201
Visible-infrared person re-identification is challenging due to the large modality gap. To bridge the gap, most studies heavily rely on the correlation of visible-infrared holistic person images, which may perform poorly under severe distribution shi
Externí odkaz:
http://arxiv.org/abs/2310.07552
In this study we perform online sea ice bias correction within a GFDL global ice-ocean model. For this, we use a convolutional neural network (CNN) which was developed in a previous study (Gregory et al., 2023) for the purpose of predicting sea ice c
Externí odkaz:
http://arxiv.org/abs/2310.02488
Data assimilation is often viewed as a framework for correcting short-term error growth in dynamical climate model forecasts. When viewed on the time scales of climate however, these short-term corrections, or analysis increments, can closely mirror
Externí odkaz:
http://arxiv.org/abs/2304.03832
Publikováno v:
In International Communications in Heat and Mass Transfer December 2024 159 Part B
Autor:
Li, Haibin, Zhou, Lichang, Cai, Yuhang, Zhang, Yongfei, Ibrahim, Bature Auwal, Feng, Zixuan, Tang, Liyun, Li, Zhigang, Yang, Fayong
Publikováno v:
In Journal of Traffic and Transportation Engineering (English Edition) October 2024 11(5):939-971
Autor:
Guo, Yantong, Xu, Jianting, Jia, Yiyang, Tian, Yuan, Zhang, Yongfei, Zhang, Jinjin, Wang, Yufeng, Chen, Lichao
Publikováno v:
In Phytomedicine October 2024 133
Knowledge graphs store a large number of factual triples while they are still incomplete, inevitably. The previous knowledge graph completion (KGC) models predict missing links between entities merely relying on fact-view data, ignoring the valuable
Externí odkaz:
http://arxiv.org/abs/2202.13785
Knowledge graph (KG) inference aims to address the natural incompleteness of KGs, including rule learning-based and KG embedding (KGE) models. However, the rule learning-based models suffer from low efficiency and generalization while KGE models lack
Externí odkaz:
http://arxiv.org/abs/2112.01040