Abstract Domain Adaptive Object Detection (DAOD) leverages a labeled domain to learn an
object detector generalizing to a novel domain free of annotations. Recent advances align …

Most object detection methods for autonomous driving usually assume a onsistent feature
distribution between training and testing data, which is not always the case when weathers …

Cross domain object detection is a realistic and challenging task in the wild. It suffers from
performance degradation due to large shift of data distributions and lack of instance-level …

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 …

Abstract Recently, DEtection TRansformer (DETR), an end-to-end object detection pipeline,
has achieved promising performance. However, it requires large-scale labeled data and …

Abstract Domain adaptive Object Detection (DAOD) leverages a labeled domain (source) to
learn an object detector generalizing to a novel domain without annotation (target). Recent …

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 …

Unsupervised domain adaptive object detection (UDA-OD) aims to learn a detector by
generalizing knowledge from a labeled source domain to an unlabeled target domain …

Under the domain shift, cross-domain few-shot object detection aims to adapt object
detectors in the target domain with a few annotated target data. There exists two significant …

Abstract Unsupervised Domain Adaptation (UDA) for object detection aims to adapt a model
trained on a source domain to detect instances from a new target domain for which …