Zobrazeno 1 - 10
of 1 651
pro vyhledávání: '"XU Wenhao"'
Autor:
XU Wenhao, TIAN Xi, Aihetaimujiang·Anwaier , QU Yuanyuan, SHI Guohai, ZHANG Hailiang, YE Dingwei
Publikováno v:
Zhongguo aizheng zazhi, Vol 32, Iss 1, Pp 68-74 (2022)
Recently, advances in machine learning and neural network technology have allowed artificial intelligence (AI) to further promote guidance of clinical diagnosis, treatment and resource expenditures. In genitourinary cancers, AI has made huge progress
Externí odkaz:
https://doaj.org/article/2a26bcf45ecd4a989633cd7f6d10650e
Autor:
Xu, Wenhao, Bagrov, Andrey A., Chowdhury, Farhan T., Smith, Luke D., Kattnig, Daniel R., Kappen, Hilbert J., Katsnelson, Mikhail I.
Magnon-condensation, which emerges in pumped bosonic systems at room temperature, continues to garner great interest for its long-lived coherence. While traditionally formulated in terms of Bose-Einstein condensation, which typically occurs at ultra-
Externí odkaz:
http://arxiv.org/abs/2411.00058
Autor:
Zhang, Yu, Pan, Changhao, Guo, Wenxiang, Li, Ruiqi, Zhu, Zhiyuan, Wang, Jialei, Xu, Wenhao, Lu, Jingyu, Hong, Zhiqing, Wang, Chuxin, Zhang, LiChao, He, Jinzheng, Jiang, Ziyue, Chen, Yuxin, Yang, Chen, Zhou, Jiecheng, Cheng, Xinyu, Zhao, Zhou
The scarcity of high-quality and multi-task singing datasets significantly hinders the development of diverse controllable and personalized singing tasks, as existing singing datasets suffer from low quality, limited diversity of languages and singer
Externí odkaz:
http://arxiv.org/abs/2409.13832
Accurate segmentation of colorectal polyps in colonoscopy images is crucial for effective diagnosis and management of colorectal cancer (CRC). However, current deep learning-based methods primarily rely on fusing RGB information across multiple scale
Externí odkaz:
http://arxiv.org/abs/2409.08501
Autor:
Xu, Wenhao, Wang, Changwei, Feng, Xuxiang, Xu, Rongtao, Huang, Longzhao, Zhang, Zherui, Guo, Li, Xu, Shibiao
Vision-language models (VLMs) have demonstrated remarkable open-vocabulary object recognition capabilities, motivating their adaptation for dense prediction tasks like segmentation. However, directly applying VLMs to such tasks remains challenging du
Externí odkaz:
http://arxiv.org/abs/2409.08468
We present CEIA, an effective framework for open-world event-based understanding. Currently training a large event-text model still poses a huge challenge due to the shortage of paired event-text data. In response to this challenge, CEIA learns to al
Externí odkaz:
http://arxiv.org/abs/2407.06611
Currently, the integration of mobile Graphical User Interfaces (GUIs) is ubiquitous in most people's daily lives. And the ongoing evolution of multimodal large-scale models, such as GPT-4v, Qwen-VL-Max, has significantly bolstered the capabilities of
Externí odkaz:
http://arxiv.org/abs/2407.04346
Risk-sensitive linear quadratic regulator is one of the most fundamental problems in risk-sensitive optimal control. In this paper, we study online adaptive control of risk-sensitive linear quadratic regulator in the finite horizon episodic setting.
Externí odkaz:
http://arxiv.org/abs/2406.05366
Autor:
Li, Yu, Zhang, Shenyu, Wu, Rui, Huang, Xiutian, Chen, Yongrui, Xu, Wenhao, Qi, Guilin, Min, Dehai
Recent advancements in generative Large Language Models(LLMs) have been remarkable, however, the quality of the text generated by these models often reveals persistent issues. Evaluating the quality of text generated by these models, especially in op
Externí odkaz:
http://arxiv.org/abs/2403.19305
Automatic methods for evaluating machine-generated texts hold significant importance due to the expanding applications of generative systems. Conventional methods tend to grapple with a lack of explainability, issuing a solitary numerical score to si
Externí odkaz:
http://arxiv.org/abs/2403.11509