UC-OWOD: Unknown-Classified Open World Object Detection

N/ACitations
Citations of this article
40Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Open World Object Detection (OWOD) is a challenging computer vision problem that requires detecting unknown objects and gradually learning the identified unknown classes. However, it cannot distinguish unknown instances as multiple unknown classes. In this work, we propose a novel OWOD problem called Unknown-Classified Open World Object Detection (UC-OWOD). UC-OWOD aims to detect unknown instances and classify them into different unknown classes. Besides, we formulate the problem and devise a two-stage object detector to solve UC-OWOD. First, unknown label-aware proposal and unknown-discriminative classification head are used to detect known and unknown objects. Then, similarity-based unknown classification and unknown clustering refinement modules are constructed to distinguish multiple unknown classes. Moreover, two novel evaluation protocols are designed to evaluate unknown-class detection. Abundant experiments and visualizations prove the effectiveness of the proposed method. Code is available at https://github.com/JohnWuzh/UC-OWOD.

Cite

CITATION STYLE

APA

Wu, Z., Lu, Y., Chen, X., Wu, Z., Kang, L., & Yu, J. (2022). UC-OWOD: Unknown-Classified Open World Object Detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13670 LNCS, pp. 193–210). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-20080-9_12

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free