Object classification is a crucial yet challenging vision ability to perfect The fundamental
objective is to educate computers to understand visuals the same way humans do. Due to …

In unsupervised domain adaptation (UDA), a model trained on source data (eg synthetic) is
adapted to target data (eg real-world) without access to target annotation. Most previous …

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 …

Although deep neural networks enable impressive visual perception performance for
autonomous driving, their robustness to varying weather conditions still requires attention …

Object detection at night is a challenging problem due to the absence of night image
annotations. Despite several domain adaptation methods, achieving high-precision results …

Abstract Domain adaptation has been vastly investigated in computer vision but still requires
access to target images at train time, which might be intractable in some uncommon …

Cross-domain weakly supervised object detection (CDWSOD) aims to adapt the detection
model to a novel target domain with easily acquired image-level annotations. How to align …

Domain Adaptive Object Detection (DAOD) focuses on improving the generalization ability
of object detectors via knowledge transfer. Recent advances in DAOD strive to change the …

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

Faster R-CNN is an algorithm development that continuously starts from CNN then R-CNN
and Faster R-CNN. The development of the algorithm is needed to test whether the heuristic …