We address the task of domain adaptation in object detection, where there is a domain gap
between a domain with annotations (source) and a domain of interest without annotations …

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 …

Cross-domain object detection is challenging, because object detection model is often
vulnerable to data variance, especially to the considerable domain shift between two …

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 …

Supervised object detection models based on deep learning technologies cannot perform
well in domain shift scenarios where annotated data for training is always insufficient. To this …

To improve the generalization of detectors, for domain adaptive object detection (DAOD),
recent advances mainly explore aligning feature-level distributions between the source and …

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 …

Recent years have witnessed great progress in object detection. However, due to the
domain shift problem, applying the knowledge of an object detector learned from one …

Robust point cloud parsing under all-weather conditions is crucial to level-5 autonomy in
autonomous driving. However, how to learn a universal 3D semantic segmentation (3DSS) …

Self-supervised learning (SSL) is a technique for learning useful representations from
unlabeled data. It has been applied effectively to domain adaptation (DA) on images and …