Zobrazeno 1 - 10
of 13 058
pro vyhledávání: '"An, Xinlong"'
Autor:
Wang, Shuai, Liu, Weiwen, Chen, Jingxuan, Gan, Weinan, Zeng, Xingshan, Yu, Shuai, Hao, Xinlong, Shao, Kun, Wang, Yasheng, Tang, Ruiming
Recent advances in foundation models, particularly Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs), facilitate intelligent agents being capable of performing complex tasks. By leveraging the ability of (M)LLMs to process and
Externí odkaz:
http://arxiv.org/abs/2411.04890
Diffusion models have recently gained recognition for generating diverse and high-quality content, especially in the domain of image synthesis. These models excel not only in creating fixed-size images but also in producing panoramic images. However,
Externí odkaz:
http://arxiv.org/abs/2410.18830
Autor:
Xiao, Youshen, Liao, Sheng, Tian, Xuanyang, Zhang, Fan, Dong, Xinlong, Jiang, Yunhui, Chen, Xiyu, Sun, Ruixi, Zhang, Yuyao, Gao, Fei
Acoustic-Resolution Photoacoustic Microscopy (AR-PAM) is promising for subcutaneous vascular imaging, but its spatial resolution is constrained by the Point Spread Function (PSF). Traditional deconvolution methods like Richardson-Lucy and model-based
Externí odkaz:
http://arxiv.org/abs/2410.19786
Road crack segmentation is critical for robotic systems tasked with the inspection, maintenance, and monitoring of road infrastructures. Existing deep learning-based methods for crack segmentation are typically trained on specific datasets, which can
Externí odkaz:
http://arxiv.org/abs/2410.09409
Recently, there have been explorations of generalist segmentation models that can effectively tackle a variety of image segmentation tasks within a unified in-context learning framework. However, these methods still struggle with task ambiguity in in
Externí odkaz:
http://arxiv.org/abs/2410.04842
Autor:
Yang, Mengmeng, Qu, Youyang, Ranbaduge, Thilina, Thapa, Chandra, Sultan, Nazatul, Ding, Ming, Suzuki, Hajime, Ni, Wei, Abuadbba, Sharif, Smith, David, Tyler, Paul, Pieprzyk, Josef, Rakotoarivelo, Thierry, Guan, Xinlong, M'rabet, Sirine
The vision for 6G aims to enhance network capabilities with faster data rates, near-zero latency, and higher capacity, supporting more connected devices and seamless experiences within an intelligent digital ecosystem where artificial intelligence (A
Externí odkaz:
http://arxiv.org/abs/2410.21986
Autor:
Zhu, Muzhi, Liu, Yang, Luo, Zekai, Jing, Chenchen, Chen, Hao, Xu, Guangkai, Wang, Xinlong, Shen, Chunhua
The Diffusion Model has not only garnered noteworthy achievements in the realm of image generation but has also demonstrated its potential as an effective pretraining method utilizing unlabeled data. Drawing from the extensive potential unveiled by t
Externí odkaz:
http://arxiv.org/abs/2410.02369
Efficient patient-doctor interaction is among the key factors for a successful disease diagnosis. During the conversation, the doctor could query complementary diagnostic information, such as the patient's symptoms, previous surgery, and other relate
Externí odkaz:
http://arxiv.org/abs/2410.03770
Autor:
Hou, Xinlong, Shen, Sen, Li, Xueshen, Gao, Xinran, Huang, Ziyi, Holiday, Steven J., Cribbet, Matthew R., White, Susan W., Sazonov, Edward, Gan, Yu
Being able to accurately monitor the screen exposure of young children is important for research on phenomena linked to screen use such as childhood obesity, physical activity, and social interaction. Most existing studies rely upon self-report or ma
Externí odkaz:
http://arxiv.org/abs/2410.01966
Autor:
Wang, Xinlong, Zhang, Xiaosong, Luo, Zhengxiong, Sun, Quan, Cui, Yufeng, Wang, Jinsheng, Zhang, Fan, Wang, Yueze, Li, Zhen, Yu, Qiying, Zhao, Yingli, Ao, Yulong, Min, Xuebin, Li, Tao, Wu, Boya, Zhao, Bo, Zhang, Bowen, Wang, Liangdong, Liu, Guang, He, Zheqi, Yang, Xi, Liu, Jingjing, Lin, Yonghua, Huang, Tiejun, Wang, Zhongyuan
While next-token prediction is considered a promising path towards artificial general intelligence, it has struggled to excel in multimodal tasks, which are still dominated by diffusion models (e.g., Stable Diffusion) and compositional approaches (e.
Externí odkaz:
http://arxiv.org/abs/2409.18869