Zobrazeno 1 - 10
of 208
pro vyhledávání: '"Bao, Fan"'
Denoising diffusion bridge models (DDBMs) are a powerful variant of diffusion models for interpolating between two arbitrary paired distributions given as endpoints. Despite their promising performance in tasks like image translation, DDBMs require a
Externí odkaz:
http://arxiv.org/abs/2405.15885
Autor:
Bao, Fan, Xiang, Chendong, Yue, Gang, He, Guande, Zhu, Hongzhou, Zheng, Kaiwen, Zhao, Min, Liu, Shilong, Wang, Yaole, Zhu, Jun
We introduce Vidu, a high-performance text-to-video generator that is capable of producing 1080p videos up to 16 seconds in a single generation. Vidu is a diffusion model with U-ViT as its backbone, which unlocks the scalability and the capability fo
Externí odkaz:
http://arxiv.org/abs/2405.04233
Recently, diffusion models have achieved great success in generative tasks. Sampling from diffusion models is equivalent to solving the reverse diffusion stochastic differential equations (SDEs) or the corresponding probability flow ordinary differen
Externí odkaz:
http://arxiv.org/abs/2311.00941
In this paper, we present ControlVideo, a novel method for text-driven video editing. Leveraging the capabilities of text-to-image diffusion models and ControlNet, ControlVideo aims to enhance the fidelity and temporal consistency of videos that alig
Externí odkaz:
http://arxiv.org/abs/2305.17098
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation
Score distillation sampling (SDS) has shown great promise in text-to-3D generation by distilling pretrained large-scale text-to-image diffusion models, but suffers from over-saturation, over-smoothing, and low-diversity problems. In this work, we pro
Externí odkaz:
http://arxiv.org/abs/2305.16213
Large-scale diffusion models like Stable Diffusion are powerful and find various real-world applications while customizing such models by fine-tuning is both memory and time inefficient. Motivated by the recent progress in natural language processing
Externí odkaz:
http://arxiv.org/abs/2303.18181
Autor:
Bao, Fan, Nie, Shen, Xue, Kaiwen, Li, Chongxuan, Pu, Shi, Wang, Yaole, Yue, Gang, Cao, Yue, Su, Hang, Zhu, Jun
This paper proposes a unified diffusion framework (dubbed UniDiffuser) to fit all distributions relevant to a set of multi-modal data in one model. Our key insight is -- learning diffusion models for marginal, conditional, and joint distributions can
Externí odkaz:
http://arxiv.org/abs/2303.06555
In an effort to further advance semi-supervised generative and classification tasks, we propose a simple yet effective training strategy called dual pseudo training (DPT), built upon strong semi-supervised learners and diffusion models. DPT operates
Externí odkaz:
http://arxiv.org/abs/2302.10586
A large-scale deep model pre-trained on massive labeled or unlabeled data transfers well to downstream tasks. Linear evaluation freezes parameters in the pre-trained model and trains a linear classifier separately, which is efficient and attractive f
Externí odkaz:
http://arxiv.org/abs/2302.02334
Autor:
Xian-Bin Zhang, Yi-Bao Fan, Rui Jing, Mikiyas Amare Getu, Wan-Ying Chen, Wei Zhang, Hong-Xia Dong, Tikam Chand Dakal, Akhtar Hayat, Hua-Jun Cai, Milad Ashrafizadeh, A. M. Abd El-Aty, Ahmet Hacimuftuoglu, Peng Liu, Tian-Feng Li, Gautam Sethi, Kwang Seok Ahn, Yavuz Nuri Ertas, Min-Jiang Chen, Jian-Song Ji, Li Ma, Peng Gong
Publikováno v:
Military Medical Research, Vol 11, Iss 1, Pp 1-20 (2024)
Abstract Neuroendocrine neoplasms (NENs) are highly heterogeneous and potentially malignant tumors arising from secretory cells of the neuroendocrine system. Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are the most common subtype of NE
Externí odkaz:
https://doaj.org/article/ed6a827f8f5c41eca1a2004d234b6cf5