Autonomous vehicles were experiencing rapid development in the past few years. However,
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …

Driven by deep learning techniques, perception technology in autonomous driving has
developed rapidly in recent years, enabling vehicles to accurately detect and interpret …

This paper presents a grounded language-image pre-training (GLIP) model for learning
object-level, language-aware, and semantic-rich visual representations. GLIP unifies object …

Humans have a natural instinct to identify unknown object instances in their environments.
The intrinsic curiosity about these unknown instances aids in learning about them, when the …

Out-of-distribution (OOD) detection has received much attention lately due to its importance
in the safe deployment of neural networks. One of the key challenges is that models lack …

Open-world object detection (OWOD) is a challenging computer vision problem, where the
task is to detect a known set of object categories while simultaneously identifying unknown …

Building reliable object detectors that can detect out-of-distribution (OOD) objects is critical
yet underexplored. One of the key challenges is that models lack supervision signals from …

Detecting out-of-distribution (OOD) objects is indispensable for safely deploying object
detectors in the wild. Although distance-based OOD detection methods have demonstrated …

Abstract Open World Object Detection (OWOD) is a new and challenging computer vision
task that bridges the gap between classic object detection (OD) benchmarks and object …

In this paper, we study the problem of unsupervised object detection from 3D point clouds in
self-driving scenes. We present a simple yet effective method that exploits (i) point clustering …