提出可解释的开放世界检测框架，让模型真正识别未知物体。
Knowing the Unknown: Interpretable Open-World Object Detection via Concept Decomposition Model
- 用概念分解模型分离特征，区分判别性、共享和背景概念。
- 未知召回率显著提升，同时减少已知与未知混淆问题。
- 提供细粒度解释，适合需要透明决策的场景如医疗、自动驾驶。
开放世界目标检测（OWOD）要求在增量检测已知类别的同时可靠识别未知物体。现有方法主要关注提升未知召回率，却忽视可解释性，常导致已知-未知混淆，降低预测可靠性。本文提出一种基于概念分解的可解释开放世界检测框架（IPOW），引入概念分解模型（CDM），将Faster R-CNN中的耦合区域提议特征显式分解为判别性、共享和背景概念。判别性概念用于增强已知类间的特征距离；共享与背景概念因具备强泛化能力，可直接迁移用于检测未知类别。借助可解释框架，我们发现未知物体落入已知类判别空间是混淆主因，为此提出概念引导修正（CGR）策略以解决该问题。大量实验证明，IPOW显著提升未知召回率并缓解混淆，同时为已知与未知预测提供概念级可解释性。
原文摘要 · Abstract (English)
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 confusion arises when unknown objects fall into the discriminative space of known classes. To address this, we propose Concept-Guided Rectification (CGR) to further resolve such confusion. Extensive experiments show that IPOW significantly improves unknown recall while mitigating confusion, and provides concept-level interpretability for both known and unknown predictions.
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