Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

The rise of multimodal large language models (MLLMs) has spurred interest in language-
based driving tasks. However existing research typically focuses on limited tasks and often …

Object detection accuracy degrades seriously in visually degraded scenes. A natural
solution is to first enhance the degraded image and then perform object detection. However …

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 …

With the emergence of foundation models, deep learning-based object detectors have
shown practical usability in closed set scenarios. However, for real-world tasks, object …

Graph neural networks (GNNs) have attracted considerable attention from the research
community. It is well established that GNNs are usually roughly divided into spatial and …

Abstract Domain generalization (DG) enables generalizing a learning machine from multiple
seen source domains to an unseen target one. The general objective of DG methods is to …

Modern detectors are mostly trained under single and limited conditions. However, object
detection faces various complex and open situations in autonomous driving, especially in …

Abstract Domain adaptive object detection (DAOD) aims to generalize detectors trained on
an annotated source domain to an unlabelled target domain. However, existing methods …