Publication Type

Journal Article

Version

acceptedVersion

Publication Date

6-2025

Abstract

Open-world object detection (OWOD) extends object detection problem to a realistic and dynamic scenario, where a detection model is required to be capable of detecting both known and unknown objects and incrementally learning newly introduced knowledge. Current OWOD models detect the unknowns that exhibit similar features to the known objects, but they suffer from a severe label bias problem, i.e., they tend to detect all regions (including unknown object regions) that are dissimilar to the known objects as part of the background. To eliminate the label bias, this article proposes a novel module, namely reconstruction error-based Weibull (REW) model, that learns an unsupervised discriminative model for recognizing true unknown objects based on prior knowledge of object occurrence frequency via Weibull modeling. The resulting model can be further refined by another module of our method, called REW-enhanced object localization network (ROLNet), which iteratively extends pseudo-unknown objects to the unlabeled regions. Experimental results show that our method 1) significantly outperforms the prior SOTA in detecting unknown objects while maintaining competitive performance of detecting known object classes on the MS COCO dataset and 2) achieves better generalization ability on the LVIS and Objects365 datasets. Code is available at https://github.com/frh23333/mepu-owod

Keywords

Open world, object detection, unsupervised learning, self-training

Discipline

Artificial Intelligence and Robotics | OS and Networks

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

IEEE Transactions on Neural Networks and Learning Systems

Volume

36

Issue

6

First Page

11340

Last Page

11354

ISSN

2162-237X

Identifier

10.1109/TNNLS.2025.3559940

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TNNLS.2025.3559940

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