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 …

Abstract Domain adaptive object detection is challenging due to distinctive data distribution
between source domain and target domain. In this paper, we propose a unified multi …

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

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 …

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 …

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

Abstract Domain Adaptive Object Detection (DAOD) models a joint distribution of images
and labels from an annotated source domain and learns a domain-invariant transformation …

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 …

Adversarial feature alignment is widely used in domain adaptive object detection. Despite
the effectiveness on CNN-based detectors, its applicability to transformer-based detectors is …

Most existing domain adaptive object detection methods exploit adversarial feature
alignment to adapt the model to a new domain. Recent advances in adversarial feature …