Existing low-light image enhancement techniques are mostly not only difficult to deal with
both visual quality and computational efficiency but also commonly invalid in unknown …

Images captured under incorrect exposures unavoidably suffer from mixed degradations of
lightness and structures. Most existing deep learning-based exposure correction methods …

This paper proposes a new framework for low-light image enhancement by simultaneously
conducting the appearance as well as structure modeling. It employs the structural feature to …

Facial Expression Recognition (FER) in the wild poses significant challenges due to realistic
occlusions, illumination, scale, and head pose variations of the facial images. In this article …

Existing instance segmentation techniques are primarily tailored for high-visibility inputs, but
their performance significantly deteriorates in extremely low-light environments. In this work …

Universal domain adaptation (UniDA) aims to transfer the knowledge learned from a label-
rich source domain to a label-scarce target domain without any constraints on the label …

Extracting useful visual cues for the downstream tasks is especially challenging under low-
light vision. Prior works create enhanced representations by either correlating visual quality …

Images captured from low-light scenes often suffer from severe degradations, including low
visibility, color casts, intensive noises, etc. These factors not only degrade image qualities …

Low-light face detection is challenging but critical for real-world applications, such as
nighttime autonomous driving and city surveillance. Current face detection models rely on …

Recent years witnessed the breakthrough of face recognition with deep convolutional neural
networks. Dozens of papers in the field of FR are published every year. Some of them were …