Zobrazeno 1 - 10
of 588
pro vyhledávání: '"Liang, Jimin"'
Abundant, well-annotated multimodal data in remote sensing are pivotal for aligning complex visual remote sensing (RS) scenes with human language, enabling the development of specialized vision language models across diverse RS interpretation tasks.
Externí odkaz:
http://arxiv.org/abs/2408.14744
The Resolution of feature maps is critical for medical image segmentation. Most of the existing Transformer-based networks for medical image segmentation are U-Net-like architecture that contains an encoder that utilizes a sequence of Transformer blo
Externí odkaz:
http://arxiv.org/abs/2207.11553
Publikováno v:
In Information Fusion October 2024 110
Autor:
Du, Getao, Zhang, Peng, Guo, Jianzhong, Zhou, Xu, Kan, Guanghan, Jia, Jiajie, Liang, Jimin, Chen, Xiaoping, Zhan, Yonghua
Publikováno v:
In Biomedical Signal Processing and Control December 2024 98
Publikováno v:
In Neuroscience 14 May 2024 546:41-52
Publikováno v:
In Journal of Information and Intelligence June 2024
Autor:
Chen, Fei, Li, Sulei, Wei, Chen, Zhang, Yue, Guo, Kaitai, Zheng, Yang, Cao, Feng, Liang, Jimin
Publikováno v:
In Biomedical Signal Processing and Control January 2024 87 Part B
Recently proposed neural architecture search (NAS) algorithms adopt neural predictors to accelerate the architecture search. The capability of neural predictors to accurately predict the performance metrics of neural architecture is critical to NAS,
Externí odkaz:
http://arxiv.org/abs/2011.00186
Autor:
Niu, Chuang, Cong, Wenxiang, Fan, Fenglei, Shan, Hongming, Li, Mengzhou, Liang, Jimin, Wang, Ge
Deep neural network based methods have achieved promising results for CT metal artifact reduction (MAR), most of which use many synthesized paired images for training. As synthesized metal artifacts in CT images may not accurately reflect the clinica
Externí odkaz:
http://arxiv.org/abs/2007.03882
Neural architecture search (NAS) is a promising method for automatically design neural architectures. NAS adopts a search strategy to explore the predefined search space to find outstanding performance architecture with the minimum searching costs. B
Externí odkaz:
http://arxiv.org/abs/2003.12857