Object detection (OD) is a crucial computer vision task that has seen the development of
many algorithms and models over the years. While the performance of current OD models …

Self-training approaches recently achieved promising results in cross-domain object
detection, where people iteratively generate pseudo labels for unlabeled target domain …

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

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 …

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

This paper presents a DETR-based method for cross-domain weakly supervised object
detection (CDWSOD), aiming at adapting the detector from source to target domain through …

In this work, we study few-shot domain adaptive object detection (FSDAOD), where only a
few target labeled images are available for training in addition to sufficient source labeled …

In self-driving cars, adverse weather (eg, fog, rain, snow, and cloud) or occlusion scenarios
result in domain shift being unavoidable in object detection. Researchers have recently …

Abstract Domain adaptive detection aims to improve the generality of a detector, learned
from the labeled source domain, on the unlabeled target domain. In this work, drawing …