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 …

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

Currently, health management driven by intelligent means is a general demand of social
systems. Although a number of researchers have paid attention to such areas, they have …

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 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 …

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 …

Cellular nuclei recognition serves as a fundamental and essential step in the workflow of
digital pathology. However, with disparate source organs and staining procedures among …