Bilevel Generative Learning for Low-Light Vision
Yingchi Liu Zhu Liu Long Ma Jinyuan Liu Xin Fan Zhongxuan Luo Risheng Liu
Recently, there has been a growing interest in constructing deep learning
schemes for Low-Light Vision (LLV). Existing techniques primarily focus on
designing task-specific and data-dependent vision models on the standard RGB
domain, which inherently contain latent data associations. In this study, we
propose a generic low-light vision solution by introducing a generative block
to convert data from the RAW to the RGB domain. This novel approach connects
diverse vision problems by explicitly depicting data generation, which is the
first in the field. To precisely characterize the latent correspondence between
the generative procedure and the vision task, we establish a bilevel model with
the parameters of the generative block defined as the upper level and the
parameters of the vision task defined as the lower level. We further develop
two types of learning strategies targeting different goals, namely low cost and
high accuracy, to acquire a new bilevel generative learning paradigm. The
generative blocks embrace a strong generalization ability in other low-light
vision tasks through the bilevel optimization on enhancement tasks. Extensive
experimental evaluations on three representative low-light vision tasks, namely
enhancement, detection, and segmentation, fully demonstrate the superiority of
our proposed approach. The code will be available at
https://github.com/Yingchi1998/BGL.