Zobrazeno 1 - 10
of 335
pro vyhledávání: '"Zhang Zeyang"'
Publikováno v:
Renmin Zhujiang, Vol 44 (2023)
This paper takes Fenghe River in Langfang City as the research object to conduct river health evaluation,which is expected to provide a direction for the future health management of Fenghe River and achieve the goal of healthy development of the wate
Externí odkaz:
https://doaj.org/article/6fb43c3b67d94b9e8a0211ceac2e4936
Autor:
Chen, Houlun, Wang, Xin, Chen, Hong, Zhang, Zeyang, Feng, Wei, Huang, Bin, Jia, Jia, Zhu, Wenwu
Existing Video Corpus Moment Retrieval (VCMR) is limited to coarse-grained understanding, which hinders precise video moment localization when given fine-grained queries. In this paper, we propose a more challenging fine-grained VCMR benchmark requir
Externí odkaz:
http://arxiv.org/abs/2410.08593
Autor:
Chen, Hong, Wang, Xin, Zhou, Yuwei, Huang, Bin, Zhang, Yipeng, Feng, Wei, Chen, Houlun, Zhang, Zeyang, Tang, Siao, Zhu, Wenwu
Multi-modal generative AI has received increasing attention in both academia and industry. Particularly, two dominant families of techniques are: i) The multi-modal large language model (MLLM) such as GPT-4V, which shows impressive ability for multi-
Externí odkaz:
http://arxiv.org/abs/2409.14993
Video generation has witnessed great success recently, but their application in generating long videos still remains challenging due to the difficulty in maintaining the temporal consistency of generated videos and the high memory cost during generat
Externí odkaz:
http://arxiv.org/abs/2407.13219
Autor:
Xie, Beini, Chang, Heng, Zhang, Ziwei, Zhang, Zeyang, Wu, Simin, Wang, Xin, Meng, Yuan, Zhu, Wenwu
Graph Neural Architecture Search (GNAS) has achieved superior performance on various graph-structured tasks. However, existing GNAS studies overlook the applications of GNAS in resource-constraint scenarios. This paper proposes to design a joint grap
Externí odkaz:
http://arxiv.org/abs/2406.16357
Autor:
Meng, Zhiming, Li, Hui, Zhang, Zeyang, Shen, Zhongwei, Yu, Yunlong, Song, Xiaoning, Wu, Xiaojun
Generative models are widely utilized to model the distribution of fused images in the field of infrared and visible image fusion. However, current generative models based fusion methods often suffer from unstable training and slow inference speed. T
Externí odkaz:
http://arxiv.org/abs/2405.20764
Autor:
Li, Peiwen, Wang, Xin, Zhang, Zeyang, Qin, Yijian, Zhang, Ziwei, Wang, Jialong, Li, Yang, Zhu, Wenwu
Graph NAS has emerged as a promising approach for autonomously designing GNN architectures by leveraging the correlations between graphs and architectures. Existing methods fail to generalize under distribution shifts that are ubiquitous in real-worl
Externí odkaz:
http://arxiv.org/abs/2405.16489
Generating customized content in videos has received increasing attention recently. However, existing works primarily focus on customized text-to-video generation for single subject, suffering from subject-missing and attribute-binding problems when
Externí odkaz:
http://arxiv.org/abs/2405.12796
Autor:
Li, Peiwen, Wang, Xin, Zhang, Zeyang, Meng, Yuan, Shen, Fang, Li, Yue, Wang, Jialong, Li, Yang, Zhu, Wenweu
In the field of Artificial Intelligence for Information Technology Operations, causal discovery is pivotal for operation and maintenance of graph construction, facilitating downstream industrial tasks such as root cause analysis. Temporal causal disc
Externí odkaz:
http://arxiv.org/abs/2404.14786
Autor:
Yao, Yang, Wang, Xin, Zhang, Zeyang, Qin, Yijian, Zhang, Ziwei, Chu, Xu, Yang, Yuekui, Zhu, Wenwu, Mei, Hong
Large language models (LLMs) have achieved great success in many fields, and recent works have studied exploring LLMs for graph discriminative tasks such as node classification. However, the abilities of LLMs for graph generation remain unexplored in
Externí odkaz:
http://arxiv.org/abs/2403.14358