Object detection is a crucial branch of computer vision that aims to locate and classify
objects in images. Using deep convolutional neural networks (CNNs) as the primary …

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable
of recognizing objects from both base and novel categories. Recent advances leverage …

Abstract Novel Class Discovery (NCD) aims to discover unknown classes without any
annotation, by exploiting the transferable knowledge already learned from a base set of …

It is a long-term vision for Autonomous Driving (AD) community that the perception models
can learn from a large-scale point cloud dataset, to obtain unified representations that can …

We introduce MOVE, a novel method to segment objects without any form of supervision.
MOVE exploits the fact that foreground objects can be shifted locally relative to their initial …

Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of
safety assurance. However objects encountered on the road exhibit a long-tailed distribution …

Novel Class Discovery (NCD) is a growing field where we are given during training a
labeled set of known classes and an unlabeled set of different classes that must be …

Abstract Machine learning holds tremendous promise for transforming the fundamental
practice of scientific discovery by virtue of its data-driven nature. With the ever-increasing …

Abstract Domain Adaptive Object Detection (DAOD) transfers an object detector to a novel
domain free of labels. However, in the real world, besides encountering novel scenes, novel …