Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Xu, Zhengqin"'
Open-vocabulary semantic segmentation seeks to label each pixel in an image with arbitrary text descriptions. Vision-language foundation models, especially CLIP, have recently emerged as powerful tools for acquiring open-vocabulary capabilities. Howe
Externí odkaz:
http://arxiv.org/abs/2405.18840
Autor:
Si, Chongjie, Wang, Xuehui, Yang, Xue, Xu, Zhengqin, Li, Qingyun, Dai, Jifeng, Qiao, Yu, Yang, Xiaokang, Shen, Wei
Adapting pre-trained foundation models for various downstream tasks has been prevalent in artificial intelligence. Due to the vast number of tasks and high costs, adjusting all parameters becomes unfeasible. To mitigate this, several fine-tuning tech
Externí odkaz:
http://arxiv.org/abs/2405.14739
Flow image super-resolution (FISR) aims at recovering high-resolution turbulent velocity fields from low-resolution flow images. Existing FISR methods mainly process the flow images in natural image patterns, while the critical and distinct flow visu
Externí odkaz:
http://arxiv.org/abs/2401.15913
Prompt learning has emerged as an effective and data-efficient technique in large Vision-Language Models (VLMs). However, when adapting VLMs to specialized domains such as remote sensing and medical imaging, domain prompt learning remains underexplor
Externí odkaz:
http://arxiv.org/abs/2312.08878
Parameter-efficient fine-tuning (PEFT) is an effective methodology to unleash the potential of large foundation models in novel scenarios with limited training data. In the computer vision community, PEFT has shown effectiveness in image classificati
Externí odkaz:
http://arxiv.org/abs/2311.17112
Large pre-trained vision-language models, such as CLIP, have shown remarkable generalization capabilities across various tasks when appropriate text prompts are provided. However, adapting these models to specific domains, like remote sensing images
Externí odkaz:
http://arxiv.org/abs/2310.07730
Segment Anything Model (SAM) has received remarkable attention as it offers a powerful and versatile solution for object segmentation in images. However, fine-tuning SAM for downstream segmentation tasks under different scenarios remains a challenge,
Externí odkaz:
http://arxiv.org/abs/2308.14604
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Autor:
Hua, Congkun1 (AUTHOR) huacongkun@shu.edu.cn, Xu, Zhengqin1 (AUTHOR), Tang, Nan1 (AUTHOR), Xu, Yehan1 (AUTHOR), Zhang, Yansheng1 (AUTHOR), Li, Changfu1 (AUTHOR) changfuli@shu.edu.cn
Publikováno v:
International Journal of Molecular Sciences. Sep2023, Vol. 24 Issue 18, p14077. 11p.
Akademický článek
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