2 research outputs found
Learning Domain Adaptive Features with Unlabeled Domain Bridges
Conventional cross-domain image-to-image translation or unsupervised domain
adaptation methods assume that the source domain and target domain are closely
related. This neglects a practical scenario where the domain discrepancy
between the source and target is excessively large. In this paper, we propose a
novel approach to learn domain adaptive features between the largely-gapped
source and target domains with unlabeled domain bridges. Firstly, we introduce
the framework of Cycle-consistency Flow Generative Adversarial Networks (CFGAN)
that utilizes domain bridges to perform image-to-image translation between two
distantly distributed domains. Secondly, we propose the Prototypical
Adversarial Domain Adaptation (PADA) model which utilizes unlabeled bridge
domains to align feature distribution between source and target with a large
discrepancy. Extensive quantitative and qualitative experiments are conducted
to demonstrate the effectiveness of our proposed models.Comment: Both authors contributed equall
Low Light Video Enhancement using Synthetic Data Produced with an Intermediate Domain Mapping
Advances in low-light video RAW-to-RGB translation are opening up the
possibility of fast low-light imaging on commodity devices (e.g. smartphone
cameras) without the need for a tripod. However, it is challenging to collect
the required paired short-long exposure frames to learn a supervised mapping.
Current approaches require a specialised rig or the use of static videos with
no subject or object motion, resulting in datasets that are limited in size,
diversity, and motion. We address the data collection bottleneck for low-light
video RAW-to-RGB by proposing a data synthesis mechanism, dubbed SIDGAN, that
can generate abundant dynamic video training pairs. SIDGAN maps videos found
'in the wild' (e.g. internet videos) into a low-light (short, long exposure)
domain. By generating dynamic video data synthetically, we enable a recently
proposed state-of-the-art RAW-to-RGB model to attain higher image quality
(improved colour, reduced artifacts) and improved temporal consistency,
compared to the same model trained with only static real video data.Comment: Accepted to ECCV 202