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 …

To promote the safe application of detectors, a task of unsupervised out-of-distribution object
detection (OOD-OD) is recently proposed, whose goal is to detect unseen OOD objects …

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 …

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 …

We show that classifiers trained with random region proposals achieve state-of-the-art Open-
world Object Detection (OWOD): they can not only maintain the accuracy of the known …

Open-world object detection (OWOD), as a more general and challenging goal, requires the
model trained from data on known objects to detect both known and unknown objects and …

Open world object detection (OWOD) has attracted extensive attention due to its
practicability in the real world. Previous OWOD works manually designed unknown-discover …

Open World Object Detection (OWOD), simulating the real dynamic world where knowledge
grows continuously, attempts to detect both known and unknown classes and incrementally …

Object detection (OD), a crucial vision task, remains challenged by the lack of large training
datasets with precise object localization labels. In this work, we propose ALWOD, a new …