Recent advances in deep learning have led to the development of accurate and efficient
models for various computer vision applications such as classification, segmentation, and …

Despite remarkable success in a variety of applications, it is well-known that deep learning
can fail catastrophically when presented with out-of-distribution data. Toward addressing …

Abstract Domain shift degrades the performance of object detection models in practical
applications. To alleviate the influence of domain shift, plenty of previous work try to …

Though unsupervised domain adaptation (UDA) has achieved very impressive progress
recently, it remains a great challenge due to missing target annotations and the rich …

The last decade has witnessed a multifold growth of image data courtesy of the emergence
of social networking services like Facebook, Instagram, LinkedIn etc. The major menace …

In recent years, deep learning technologies for object detection have made great progress
and have powered the emergence of state-of-the-art models to address object detection …

The recent detection transformer (DETR) simplifies the object detection pipeline by removing
hand-crafted designs and hyperparameters as employed in conventional two-stage object …

Domain Adaptive Object Detection (DAOD) generalizes the object detector from an
annotated domain to a label-free novel one. Recent works estimate prototypes (class …

Nowadays, deep learning techniques are widely used for lane detection, but application in
low-light conditions remains a challenge until this day. Although multi-task learning and …

Face detection in low light scenarios is challenging but vital to many practical applications,
eg, surveillance video, autonomous driving at night. Most existing face detectors heavily rely …