Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Lao, Qicheng"'
Autor:
Chen, Mengmeng, Wu, Xiaohu, Tang, Xiaoli, He, Tiantian, Ong, Yew-Soon, Liu, Qiqi, Lao, Qicheng, Yu, Han
Federated learning (FL) is a machine learning paradigm that allows multiple FL participants (FL-PTs) to collaborate on training models without sharing private data. Due to data heterogeneity, negative transfer may occur in the FL training process. Th
Externí odkaz:
http://arxiv.org/abs/2410.19321
Autor:
Li, Zhilong, Wu, Xiaohu, Tang, Xiaoli, He, Tiantian, Ong, Yew-Soon, Chen, Mengmeng, Liu, Qiqi, Lao, Qicheng, Yu, Han
There is growing research interest in measuring the statistical heterogeneity of clients' local datasets. Such measurements are used to estimate the suitability for collaborative training of personalized federated learning (PFL) models. Currently, th
Externí odkaz:
http://arxiv.org/abs/2410.07286
Autor:
Zheng, Xiuqi, Zhang, Yuhang, Zhang, Haoran, Liang, Hongrui, Bao, Xueqi, Jiang, Zhuqing, Lao, Qicheng
Adapting large pre-trained foundation models, e.g., SAM, for medical image segmentation remains a significant challenge. A crucial step involves the formulation of a series of specialized prompts that incorporate specific clinical instructions. Past
Externí odkaz:
http://arxiv.org/abs/2409.00695
Autor:
Wang, Junjie, Yang, Guangjing, Chen, Wentao, Yi, Huahui, Wu, Xiaohu, Lin, Zhouchen, Lao, Qicheng
In response to the challenges posed by the extensive parameter updates required for full fine-tuning of large-scale pre-trained models, parameter-efficient fine-tuning (PEFT) methods, exemplified by Low-Rank Adaptation (LoRA), have emerged. LoRA simp
Externí odkaz:
http://arxiv.org/abs/2405.18897
Autor:
Wang, Xiaosong, Zhang, Xiaofan, Wang, Guotai, He, Junjun, Li, Zhongyu, Zhu, Wentao, Guo, Yi, Dou, Qi, Li, Xiaoxiao, Wang, Dequan, Hong, Liang, Lao, Qicheng, Ruan, Tong, Zhou, Yukun, Li, Yixue, Zhao, Jie, Li, Kang, Sun, Xin, Zhu, Lifeng, Zhang, Shaoting
The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and many other research areas. However, domain-specific applications of s
Externí odkaz:
http://arxiv.org/abs/2402.18028
Autor:
Jiang, Zekun, Cheng, Dongjie, Qin, Ziyuan, Gao, Jun, Lao, Qicheng, Ismoilovich, Abdullaev Bakhrom, Gayrat, Urazboev, Elyorbek, Yuldashov, Habibullo, Bekchanov, Tang, Defu, Wei, LinJing, Li, Kang, Zhang, Le
This study presents a novel multimodal medical image zero-shot segmentation algorithm named the text-visual-prompt segment anything model (TV-SAM) without any manual annotations. The TV-SAM incorporates and integrates the large language model GPT-4,
Externí odkaz:
http://arxiv.org/abs/2402.15759
Masked autoencoder (MAE) has attracted unprecedented attention and achieves remarkable performance in many vision tasks. It reconstructs random masked image patches (known as proxy task) during pretraining and learns meaningful semantic representatio
Externí odkaz:
http://arxiv.org/abs/2306.08249
Autor:
Yi, Huahui, Qin, Ziyuan, Xu, Wei, Guo, Miaotian, Wang, Kun, Zhang, Shaoting, Li, Kang, Lao, Qicheng
Large pre-trained vision-language models have shown great prominence in transferring pre-acquired knowledge to various domains and downstream tasks with appropriate prompting or tuning. Existing prevalent tuning methods can be generally categorized i
Externí odkaz:
http://arxiv.org/abs/2305.18993
The Segment Anything Model (SAM) made an eye-catching debut recently and inspired many researchers to explore its potential and limitation in terms of zero-shot generalization capability. As the first promptable foundation model for segmentation task
Externí odkaz:
http://arxiv.org/abs/2305.00035
Autor:
Yi, Huahui, Qin, Ziyuan, Lao, Qicheng, Xu, Wei, Jiang, Zekun, Wang, Dequan, Zhang, Shaoting, Li, Kang
Inevitable domain and task discrepancies in real-world scenarios can impair the generalization performance of the pre-trained deep models for medical data. Therefore, we audaciously propose that we should build a general-purpose medical AI system tha
Externí odkaz:
http://arxiv.org/abs/2303.06580