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[2602.20616] Knowing the Unknown: Interpretable Open-World Object Detection via Concept Decomposition Model
Summary
This article presents a novel approach to open-world object detection through an interpretable framework that enhances the identification of known and unknown objects using a Concept Decomposition Model.
Why It Matters
As the demand for robust object detection systems increases, particularly in dynamic environments, this research addresses the critical challenge of distinguishing between known and unknown objects. The proposed IPOW framework not only improves detection accuracy but also enhances interpretability, which is essential for trust in AI systems.
Key Takeaways
- Introduces an interpretable framework for open-world object detection.
- Enhances detection of unknown objects while reducing known-unknown confusion.
- Utilizes a Concept Decomposition Model to improve feature discrimination.
- Implements Concept-Guided Rectification to resolve classification ambiguities.
- Demonstrates significant improvements in experimental results.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.20616 (cs) [Submitted on 24 Feb 2026] Title:Knowing the Unknown: Interpretable Open-World Object Detection via Concept Decomposition Model Authors:Xueqiang Lv, Shizhou Zhang, Yinghui Xing, Di Xu, Peng Wang, Yanning Zhang View a PDF of the paper titled Knowing the Unknown: Interpretable Open-World Object Detection via Concept Decomposition Model, by Xueqiang Lv and 5 other authors View PDF HTML (experimental) Abstract:Open-world object detection (OWOD) requires incrementally detecting known categories while reliably identifying unknown objects. Existing methods primarily focus on improving unknown recall, yet overlook interpretability, often leading to known-unknown confusion and reduced prediction reliability. This paper aims to make the entire OWOD framework interpretable, enabling the detector to truly "knowing the unknown". To this end, we propose a concept-driven InterPretable OWOD framework(IPOW) by introducing a Concept Decomposition Model (CDM) for OWOD, which explicitly decomposes the coupled RoI features in Faster R-CNN into discriminative, shared, and background concepts. Discriminative concepts identify the most discriminative features to enlarge the distances between known categories, while shared and background concepts, due to their strong generalization ability, can be readily transferred to detect unknown categories. Leveraging the interpretable framework, we identify that known-unknown c...
