all AI news
AesUST: Towards Aesthetic-Enhanced Universal Style Transfer. (arXiv:2208.13016v1 [cs.CV])
Aug. 30, 2022, 1:14 a.m. | Zhizhong Wang, Zhanjie Zhang, Lei Zhao, Zhiwen Zuo, Ailin Li, Wei Xing, Dongming Lu
cs.CV updates on arXiv.org arxiv.org
Recent studies have shown remarkable success in universal style transfer
which transfers arbitrary visual styles to content images. However, existing
approaches suffer from the aesthetic-unrealistic problem that introduces
disharmonious patterns and evident artifacts, making the results easy to spot
from real paintings. To address this limitation, we propose AesUST, a novel
Aesthetic-enhanced Universal Style Transfer approach that can generate
aesthetically more realistic and pleasing results for arbitrary styles.
Specifically, our approach introduces an aesthetic discriminator to learn the
universal human-delightful …
More from arxiv.org / cs.CV updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Senior Data Analyst - SQL
@ Experian | Heredia, Costa Rica
Lead Business Intelligence Developer
@ L.A. Care Health Plan | Los Angeles, CA, US, 90017
(USA) Senior Manager, Data Analytics
@ Walmart | (USA) AR BENTONVILLE Home Office J Street Offices, Suite #2
Autonomous Haulage System Application Specialist
@ Komatsu | Belo Horizonte, BR
Machine Learning Engineer
@ GFT Technologies | Alcobendas, M, ES, 28108