all AI news
CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer. (arXiv:2207.04808v4 [cs.CV] UPDATED)
July 20, 2022, 1:13 a.m. | Zijie Wu, Zhen Zhu, Junping Du, Xiang Bai
cs.CV updates on arXiv.org arxiv.org
In this paper, we aim to devise a universally versatile style transfer method
capable of performing artistic, photo-realistic, and video style transfer
jointly, without seeing videos during training. Previous single-frame methods
assume a strong constraint on the whole image to maintain temporal consistency,
which could be violated in many cases. Instead, we make a mild and reasonable
assumption that global inconsistency is dominated by local inconsistencies and
devise a generic Contrastive Coherence Preserving Loss (CCPL) applied to local
patches. CCPL …
More from arxiv.org / cs.CV updates on arXiv.org
Jobs in AI, ML, Big Data
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Lead Software Engineer - Artificial Intelligence, LLM
@ OpenText | Hyderabad, TG, IN
Lead Software Engineer- Python Data Engineer
@ JPMorgan Chase & Co. | GLASGOW, LANARKSHIRE, United Kingdom
Data Analyst (m/w/d)
@ Collaboration Betters The World | Berlin, Germany
Data Engineer, Quality Assurance
@ Informa Group Plc. | Boulder, CO, United States
Director, Data Science - Marketing
@ Dropbox | Remote - Canada