The incorporation of physical information in machine learning frameworks is opening and
transforming many application domains. Here the learning process is augmented through …

In real scenes, due to the problems of low light and unsuitable views, the images often
exhibit a variety of degradations, such as low contrast, color distortion, and noise. These …

Object detection accuracy degrades seriously in visually degraded scenes. A natural
solution is to first enhance the degraded image and then perform object detection. However …

Abstract Domain adaptive object detection (DAOD) aims to alleviate transfer performance
degradation caused by the cross-domain discrepancy. However, most existing DAOD …

Adverse weather conditions in real-world scenarios lead to performance degradation of
deep learning-based detection models. A well-known method is to use image restoration …

Convolutional neural network (CNN)-based autonomous driving object detection algorithms
have excellent detection results on conventional datasets, but the detector performance can …

For autonomous driving, perception is a primary and essential element that fundamentally
deals with the insight into the ego vehicle's environment through sensors. Perception is …

Although image restoration has achieved significant progress, its potential to assist object
detectors in adverse imaging conditions lacks enough attention. It is reported that the …

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 …

Recently, the deep learning-based object detection methods have achieved a great
success. However, the performance of such techniques deteriorates on the images captured …