CyEDA : CYCLE OBJECT EDGE CONSISTENCY DOMAIN ADAPTATION
Paper i proceeding, 2022
object detection/segmentation network and annotation labels. In this work, we propose a novel method namely CyEDA to perform global level domain adaptation that taking care of image contents without any pre-train networks integration or annotation labels. That is, we introduce masking and cycle-object edge consistency loss which exploit the preservation of image objects. We show that our approach is able to outperform other SOTAs in terms of image quality and FID score in both BDD100K and GTA datasets.
domain adaptation
image-to-image translation
Författare
Jing Chong Beh
University of Malaya
Kam Woh Ng
University of Surrey
Jie Long Kew
University of Malaya
Che-Tsung Lin
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Shang-Hong Lai
Microsoft AI R&D Center
National Tsing Hua University
Christopher Zach
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Proceedings - International Conference on Image Processing, ICIP
15224880 (ISSN)
2986-29909781665496209 (ISBN)
Bordeaux, France,
Ämneskategorier
Signalbehandling
Datorseende och robotik (autonoma system)
DOI
10.1109/ICIP46576.2022.9897493