Zobrazeno 1 - 10
of 1 214
pro vyhledávání: '"WANG, YONGXIN"'
Autor:
Wang, Yongxin, Cao, Meng, Lin, Haokun, Han, Mingfei, Ma, Liang, Jiang, Jin, Cheng, Yuhao, Liang, Xiaodan
Multimodal large language models (MLLMs) have achieved remarkable progress on various visual question answering and reasoning tasks leveraging instruction fine-tuning specific datasets. They can also learn from preference data annotated by human to e
Externí odkaz:
http://arxiv.org/abs/2412.04903
X-Ray microtomography of mercury intruded compacted clay: An insight into the geometry of macropores
Autor:
Yuan, Shengyang, Liu, Xianfeng, Wang, Yongxin, Delage, Pierre, Aimedieu, Patrick, Buzzi, Olivier
Publikováno v:
Applied Clay Science, 2022, 227, pp.106573
Soil properties, such as wetting collapse behavior and permeability, are strongly correlated to the soil microstructure. To date, several techniques including mercury intrusion porosimetry (MIP), can be used to characterize the microstructure of soil
Externí odkaz:
http://arxiv.org/abs/2407.21083
Autor:
Yun, Sukmin, Lin, Haokun, Thushara, Rusiru, Bhat, Mohammad Qazim, Wang, Yongxin, Jiang, Zutao, Deng, Mingkai, Wang, Jinhong, Tao, Tianhua, Li, Junbo, Li, Haonan, Nakov, Preslav, Baldwin, Timothy, Liu, Zhengzhong, Xing, Eric P., Liang, Xiaodan, Shen, Zhiqiang
Multimodal large language models (MLLMs) have shown impressive success across modalities such as image, video, and audio in a variety of understanding and generation tasks. However, current MLLMs are surprisingly poor at understanding webpage screens
Externí odkaz:
http://arxiv.org/abs/2406.20098
Autor:
Wang, Yongxin1 (AUTHOR) 2320600016@stu.hrbust.edu.cn, Jiang, He1 (AUTHOR) 2220600076@stu.hrbust.edu.cn, Sun, Yutong2 (AUTHOR) 2303010617@stu.hrbust.edu.cn, Xu, Longqi1 (AUTHOR)
Publikováno v:
Sensors (14248220). Nov2024, Vol. 24 Issue 21, p6921. 19p.
Recently, deep cross-modal hashing has gained increasing attention. However, in many practical cases, data are distributed and cannot be collected due to privacy concerns, which greatly reduces the cross-modal hashing performance on each client. And
Externí odkaz:
http://arxiv.org/abs/2210.15678
The Zero-Shot Sketch-based Image Retrieval (ZS-SBIR) is a challenging task because of the large domain gap between sketches and natural images as well as the semantic inconsistency between seen and unseen categories. Previous literature bridges seen
Externí odkaz:
http://arxiv.org/abs/2204.05666
Publikováno v:
In Applied Materials Today December 2024 41
Publikováno v:
In Journal of Alloys and Compounds 25 November 2024 1006
Autor:
Zhang, Yuhang, Cheng, Yongfa, Zhang, Qixiang, He, Wenbin, Wang, Yongxin, Ma, Yanan, Yu, Gengchen, Wang, Mengjie, Gao, Bowen, Huang, Tao, Ge, Binghui, Gao, Yihua, Wen, Li, Wang, Siliang, Yue, Yang
Publikováno v:
In Energy Storage Materials November 2024 73
Autor:
Liu, Jing, Song, Zhepeng, Wang, Yongxin, Nishimura, Kazuhito, Zhang, Jian, Lu, Xiaojiang, Pang, Xuming
Publikováno v:
In Journal of Materials Research and Technology November-December 2024 33:6035-6044