Recent advancements in perception for autonomous driving are driven by deep learning. In
order to achieve robust and accurate scene understanding, autonomous vehicles are …

Capturing uncertainty in object detection is indispensable for safe autonomous driving. In
recent years, deep learning has become the de-facto approach for object detection, and …

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 …

Modern object detectors have achieved impressive progress under the close-set setup.
However, open-set object detection (OSOD) remains challenging since objects of unknown …

Abstract Open World Object Detection (OWOD) is a challenging computer vision problem
that requires detecting unknown objects and gradually learning the identified unknown …

When incorporating deep neural networks into robotic systems, a major challenge is the lack
of uncertainty measures associated with their output predictions. Methods for uncertainty …