Abstract Unsupervised Domain Adaptation (UDA) technique has been explored in 3D cross-
domain tasks recently. Though preliminary progress has been made, the performance gap …

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 …

The complexity and diversity of scenarios, along with the presence of environmental noise in
factory settings, pose significant challenges to the implementation of deep learning-based …

Few-shot fine-grained learning aims to classify a query image into one of a set of support
categories with fine-grained differences. Although learning different objects' local differences …

Few-shot object detection (FSOD) aims to localize and recognize potential objects of interest
only by using a few annotated data, and it is beneficial for remote sensing images (RSIs) …

Abstract Domain Adaptation (DA) has been widely explored and made significant progress
on cross-domain 3D tasks recently. Despite being effective, existing works fail to deal with …

In typical unsupervised domain adaptive object detection, it is assumed that extensive
unlabeled training data from the target domain can be easily obtained. However, in some …

Unsupervised domain adaptive object detection (UDA-OD) is a challenging task that aims to
improve the generalization of detectors across domains. Although the existing UDA-OD …

Object detection applications, especially in autonomous driving, have drawn attention due to
the advancements in deep learning. Additionally, with continuous improvements in classical …

Domain adaptation (DA) aims to enhance the feature transferability of a model across
different domains with feature distribution differences, which has been widely explored in …