Zobrazeno 1 - 10
of 2 786
pro vyhledávání: '"LI Jiahao"'
Autor:
Su, Wenxin, Tang, Song, Liu, Xiaofeng, Yi, Xiaojing, Ye, Mao, Zu, Chunxiao, Li, Jiahao, Zhu, Xiatian
Domain shift (the difference between source and target domains) poses a significant challenge in clinical applications, e.g., Diabetic Retinopathy (DR) grading. Despite considering certain clinical requirements, like source data privacy, conventional
Externí odkaz:
http://arxiv.org/abs/2412.01203
Autor:
Li, Chao, Li, Jiahao, Zhang, Jinwei, Solomon, Eddy, Dimov, Alexey V., Spincemaille, Pascal, Nguyen, Thanh D., Prince, Martin R., Wang, Yi
Purpose: To develop an MRI technique for free-breathing 3D whole-liver quantification of water T1, water T2, proton density fat fraction (PDFF), R2*. Methods: An Eight-echo spoiled gradient echo pulse sequence with spiral readout was developed by int
Externí odkaz:
http://arxiv.org/abs/2410.15175
Autor:
Li, Chao, Zhang, Jinwei, Dimov, Alexey V., de González, Anne K. Koehne, Prince, Martin R., Li, Jiahao, Romano, Dominick, Spincemaille, Pascal, Nguyen, Thanh D., Brittenham, Gary M., Wang, Yi
In chronic liver disease, liver fibrosis develops as excessive deposition of extracellular matrix macromolecules, predominantly collagens, progressively form fibrous scars that disrupt the hepatic architecture, and fibrosis, iron, and fat are interre
Externí odkaz:
http://arxiv.org/abs/2410.03127
People often capture memories through photos, screenshots, and videos. While existing AI-based tools enable querying this data using natural language, they mostly only support retrieving individual pieces of information like certain objects in photos
Externí odkaz:
http://arxiv.org/abs/2409.08250
Publikováno v:
2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Tianjin, China, 2024, pp. 254-259
Graph clustering, a classical task in graph learning, involves partitioning the nodes of a graph into distinct clusters. This task has applications in various real-world scenarios, such as anomaly detection, social network analysis, and community dis
Externí odkaz:
http://arxiv.org/abs/2408.04339
Autor:
Li, Jiahao Nick, Chong, Toby, Zhou, Zhongyi, Yoshida, Hironori, Yatani, Koji, Chen, Xiang 'Anthony', Igarashi, Takeo
Object pose estimation plays a vital role in mixed-reality interactions when users manipulate tangible objects as controllers. Traditional vision-based object pose estimation methods leverage 3D reconstruction to synthesize training data. However, th
Externí odkaz:
http://arxiv.org/abs/2407.08081
Autor:
Li, Chao, Zhang, Jinwei, Zhang, Hang, Li, Jiahao, Spincemaille, Pascal, Nguyen, Thanh D., Wang, Yi
Purpose: To develop a pipeline for motion artifact correction in mGRE and quantitative susceptibility mapping (QSM). Methods: Deep learning is integrated with autofocus to improve motion artifact suppression, which is applied QSM of patients with Par
Externí odkaz:
http://arxiv.org/abs/2405.16664
In-context learning (ICL), which promotes inference with several demonstrations, has become a widespread paradigm to stimulate LLM capabilities for downstream tasks. Due to context length constraints, it cannot be further improved in spite of more tr
Externí odkaz:
http://arxiv.org/abs/2405.10738
Autor:
Li, Zhimin, Zhang, Jianwei, Lin, Qin, Xiong, Jiangfeng, Long, Yanxin, Deng, Xinchi, Zhang, Yingfang, Liu, Xingchao, Huang, Minbin, Xiao, Zedong, Chen, Dayou, He, Jiajun, Li, Jiahao, Li, Wenyue, Zhang, Chen, Quan, Rongwei, Lu, Jianxiang, Huang, Jiabin, Yuan, Xiaoyan, Zheng, Xiaoxiao, Li, Yixuan, Zhang, Jihong, Zhang, Chao, Chen, Meng, Liu, Jie, Fang, Zheng, Wang, Weiyan, Xue, Jinbao, Tao, Yangyu, Zhu, Jianchen, Liu, Kai, Lin, Sihuan, Sun, Yifu, Li, Yun, Wang, Dongdong, Chen, Mingtao, Hu, Zhichao, Xiao, Xiao, Chen, Yan, Liu, Yuhong, Liu, Wei, Wang, Di, Yang, Yong, Jiang, Jie, Lu, Qinglin
We present Hunyuan-DiT, a text-to-image diffusion transformer with fine-grained understanding of both English and Chinese. To construct Hunyuan-DiT, we carefully design the transformer structure, text encoder, and positional encoding. We also build f
Externí odkaz:
http://arxiv.org/abs/2405.08748
Autor:
Qian, Chen, Li, Jiahao, Dang, Yufan, Liu, Wei, Wang, YiFei, Xie, Zihao, Chen, Weize, Yang, Cheng, Zhang, Yingli, Liu, Zhiyuan, Sun, Maosong
Autonomous agents powered by large language models (LLMs) show significant potential for achieving high autonomy in various scenarios such as software development. Recent research has shown that LLM agents can leverage past experiences to reduce erro
Externí odkaz:
http://arxiv.org/abs/2405.04219