Zobrazeno 1 - 10
of 77
pro vyhledávání: '"Cui Yufeng"'
Publikováno v:
中西医结合护理, Vol 9, Iss 8, Pp 261-265 (2023)
Cancer-related fatigue is a severe form of fatigue among people with cancer, influencing the treatment outcome and quality of life of the patient. This paper reviewed the studies about cancer-related fatigue and related systematic nursing interventio
Externí odkaz:
https://doaj.org/article/b07f099cc4a945c3a4a9b85803d40121
Publikováno v:
Zhongguo shipin weisheng zazhi, Vol 35, Iss 6, Pp 903-908 (2023)
ObjectiveTo assess the impact of reducing sodium content in instant noodles on sodium intake levels among instant noodle consumers under different scenarios， the dietary sodium intake level of instant noodle consumers in China was analyzed.MethodsD
Externí odkaz:
https://doaj.org/article/004d6e4a8b78463b99b016427d54d186
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
Existing vision-language models (VLMs) mostly rely on vision encoders to extract visual features followed by large language models (LLMs) for visual-language tasks. However, the vision encoders set a strong inductive bias in abstracting visual repres
Externí odkaz:
http://arxiv.org/abs/2406.11832
Autor:
Sun, Quan, Wang, Jinsheng, Yu, Qiying, Cui, Yufeng, Zhang, Fan, Zhang, Xiaosong, Wang, Xinlong
Scaling up contrastive language-image pretraining (CLIP) is critical for empowering both vision and multimodal models. We present EVA-CLIP-18B, the largest and most powerful open-source CLIP model to date, with 18-billion parameters. With only 6-bill
Externí odkaz:
http://arxiv.org/abs/2402.04252
Autor:
Sun, Quan, Cui, Yufeng, Zhang, Xiaosong, Zhang, Fan, Yu, Qiying, Luo, Zhengxiong, Wang, Yueze, Rao, Yongming, Liu, Jingjing, Huang, Tiejun, Wang, Xinlong
The human ability to easily solve multimodal tasks in context (i.e., with only a few demonstrations or simple instructions), is what current multimodal systems have largely struggled to imitate. In this work, we demonstrate that the task-agnostic in-
Externí odkaz:
http://arxiv.org/abs/2312.13286
Autor:
Yu, Qiying, Sun, Quan, Zhang, Xiaosong, Cui, Yufeng, Zhang, Fan, Cao, Yue, Wang, Xinlong, Liu, Jingjing
Large multimodal models demonstrate remarkable generalist ability to perform diverse multimodal tasks in a zero-shot manner. Large-scale web-based image-text pairs contribute fundamentally to this success, but suffer from excessive noise. Recent stud
Externí odkaz:
http://arxiv.org/abs/2310.20550
Autor:
Sun, Quan, Yu, Qiying, Cui, Yufeng, Zhang, Fan, Zhang, Xiaosong, Wang, Yueze, Gao, Hongcheng, Liu, Jingjing, Huang, Tiejun, Wang, Xinlong
We present Emu, a Transformer-based multimodal foundation model, which can seamlessly generate images and texts in multimodal context. This omnivore model can take in any single-modality or multimodal data input indiscriminately (e.g., interleaved im
Externí odkaz:
http://arxiv.org/abs/2307.05222
Autor:
Li, Yangguang, Huang, Bin, Chen, Zeren, Cui, Yufeng, Liang, Feng, Shen, Mingzhu, Liu, Fenggang, Xie, Enze, Sheng, Lu, Ouyang, Wanli, Shao, Jing
Publikováno v:
Transactions on Pattern Analysis and Machine Intelligence 2024
Recently, perception task based on Bird's-Eye View (BEV) representation has drawn more and more attention, and BEV representation is promising as the foundation for next-generation Autonomous Vehicle (AV) perception. However, most existing BEV soluti
Externí odkaz:
http://arxiv.org/abs/2301.12511
Contrastive Language-Image Pretraining (CLIP) has emerged as a novel paradigm to learn visual models from language supervision. While researchers continue to push the frontier of CLIP, reproducing these works remains challenging. This is because rese
Externí odkaz:
http://arxiv.org/abs/2203.05796