Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Guo, Yuanfan"'
Autor:
Wang, Cong, Gu, Jiaxi, Hu, Panwen, Zhao, Haoyu, Guo, Yuanfan, Han, Jianhua, Xu, Hang, Liang, Xiaodan
Following the advancements in text-guided image generation technology exemplified by Stable Diffusion, video generation is gaining increased attention in the academic community. However, relying solely on text guidance for video generation has seriou
Externí odkaz:
http://arxiv.org/abs/2408.13005
Autor:
Fang, Guian, Yan, Wenbiao, Guo, Yuanfan, Han, Jianhua, Jiang, Zutao, Xu, Hang, Liao, Shengcai, Liang, Xiaodan
Text-to-image diffusion models have significantly advanced in conditional image generation. However, these models usually struggle with accurately rendering images featuring humans, resulting in distorted limbs and other anomalies. This issue primari
Externí odkaz:
http://arxiv.org/abs/2407.06937
Artifact-free super-resolution (SR) aims to translate low-resolution images into their high-resolution counterparts with a strict integrity of the original content, eliminating any distortions or synthetic details. While traditional diffusion-based S
Externí odkaz:
http://arxiv.org/abs/2403.16643
Autor:
Lu, Guansong, Guo, Yuanfan, Han, Jianhua, Niu, Minzhe, Zeng, Yihan, Xu, Songcen, Huang, Zeyi, Zhong, Zhao, Zhang, Wei, Xu, Hang
Current large-scale diffusion models represent a giant leap forward in conditional image synthesis, capable of interpreting diverse cues like text, human poses, and edges. However, their reliance on substantial computational resources and extensive d
Externí odkaz:
http://arxiv.org/abs/2312.16486
Stable diffusion, a generative model used in text-to-image synthesis, frequently encounters resolution-induced composition problems when generating images of varying sizes. This issue primarily stems from the model being trained on pairs of single-sc
Externí odkaz:
http://arxiv.org/abs/2308.16582
Modeling spatial relationship in the data remains critical across many different tasks, such as image classification, semantic segmentation and protein structure understanding. Previous works often use a unified solution like relative positional enco
Externí odkaz:
http://arxiv.org/abs/2211.12941
Autor:
Xu, Minghao, Guo, Yuanfan, Zhu, Xuanyu, Li, Jiawen, Sun, Zhenbang, Tang, Jian, Xu, Yi, Ni, Bingbing
Learning self-supervised image representations has been broadly studied to boost various visual understanding tasks. Existing methods typically learn a single level of image semantics like pairwise semantic similarity or image clustering patterns. Ho
Externí odkaz:
http://arxiv.org/abs/2205.13159
Much progress has been made in the deep neural network (DNN) based diagnosis of mass lesions breast ultrasound (BUS) images. However, the non-mass lesion is less investigated because of the limited data. Based on the insight that mass data is suffici
Externí odkaz:
http://arxiv.org/abs/2204.08478
Previous deep learning based Computer Aided Diagnosis (CAD) system treats multiple views of the same lesion as independent images. Since an ultrasound image only describes a partial 2D projection of a 3D lesion, such paradigm ignores the semantic rel
Externí odkaz:
http://arxiv.org/abs/2204.08477
Hierarchical semantic structures naturally exist in an image dataset, in which several semantically relevant image clusters can be further integrated into a larger cluster with coarser-grained semantics. Capturing such structures with image represent
Externí odkaz:
http://arxiv.org/abs/2202.00455