Zobrazeno 1 - 10
of 189
pro vyhledávání: '"CHEN Meiqi"'
Publikováno v:
Di-san junyi daxue xuebao, Vol 44, Iss 6, Pp 595-600 (2022)
Objective To investigate the effectiveness of pulse pressure variability (PPV) measured by continuous non-invasive arterial pressure (CNAP) monitor in guidance of fluid resuscitation in postoperative hypovolemic patients. Methods A prospective random
Externí odkaz:
https://doaj.org/article/713e5bfbd9c7446c952fbb6bd4cca854
This study presents a novel evaluation framework for the Vision-Language Navigation (VLN) task. It aims to diagnose current models for various instruction categories at a finer-grained level. The framework is structured around the context-free gramma
Externí odkaz:
http://arxiv.org/abs/2409.17313
Autor:
Ma, Yubo, Zang, Yuhang, Chen, Liangyu, Chen, Meiqi, Jiao, Yizhu, Li, Xinze, Lu, Xinyuan, Liu, Ziyu, Ma, Yan, Dong, Xiaoyi, Zhang, Pan, Pan, Liangming, Jiang, Yu-Gang, Wang, Jiaqi, Cao, Yixin, Sun, Aixin
Understanding documents with rich layouts and multi-modal components is a long-standing and practical task. Recent Large Vision-Language Models (LVLMs) have made remarkable strides in various tasks, particularly in single-page document understanding
Externí odkaz:
http://arxiv.org/abs/2407.01523
Causal reasoning is fundamental to human intelligence and crucial for effective decision-making in real-world environments. Despite recent advancements in large vision-language models (LVLMs), their ability to comprehend causality remains unclear. Pr
Externí odkaz:
http://arxiv.org/abs/2406.19131
Autor:
Chen, Sirui, Peng, Bo, Chen, Meiqi, Wang, Ruiqi, Xu, Mengying, Zeng, Xingyu, Zhao, Rui, Zhao, Shengjie, Qiao, Yu, Lu, Chaochao
Causal reasoning is viewed as crucial for achieving human-level machine intelligence. Recent advances in language models have expanded the horizons of artificial intelligence across various domains, sparking inquiries into their potential for causal
Externí odkaz:
http://arxiv.org/abs/2405.00622
Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective
Recent advancements in Large Language Models (LLMs) have facilitated the development of Multimodal LLMs (MLLMs). Despite their impressive capabilities, MLLMs often suffer from an over-reliance on unimodal biases (e.g., language bias and vision bias),
Externí odkaz:
http://arxiv.org/abs/2403.18346
Autor:
Lu, Chaochao, Qian, Chen, Zheng, Guodong, Fan, Hongxing, Gao, Hongzhi, Zhang, Jie, Shao, Jing, Deng, Jingyi, Fu, Jinlan, Huang, Kexin, Li, Kunchang, Li, Lijun, Wang, Limin, Sheng, Lu, Chen, Meiqi, Zhang, Ming, Ren, Qibing, Chen, Sirui, Gui, Tao, Ouyang, Wanli, Wang, Yali, Teng, Yan, Wang, Yaru, Wang, Yi, He, Yinan, Wang, Yingchun, Wang, Yixu, Zhang, Yongting, Qiao, Yu, Shen, Yujiong, Mou, Yurong, Chen, Yuxi, Zhang, Zaibin, Shi, Zhelun, Yin, Zhenfei, Wang, Zhipin
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents. However, there is still a wide gap between the performance of recent MLLM-based applications and the ex
Externí odkaz:
http://arxiv.org/abs/2401.15071
Event relations are crucial for narrative understanding and reasoning. Governed by nuanced logic, event relation extraction (ERE) is a challenging task that demands thorough semantic understanding and rigorous logical reasoning. In this paper, we con
Externí odkaz:
http://arxiv.org/abs/2310.09158
Document-level Event Causality Identification (DECI) aims to identify causal relations between event pairs in a document. It poses a great challenge of across-sentence reasoning without clear causal indicators. In this paper, we propose a novel Event
Externí odkaz:
http://arxiv.org/abs/2204.07434
In this paper, we propose an effective yet efficient model PAIE for both sentence-level and document-level Event Argument Extraction (EAE), which also generalizes well when there is a lack of training data. On the one hand, PAIE utilizes prompt tunin
Externí odkaz:
http://arxiv.org/abs/2202.12109