EviAttr-OW: Evidential Attribute Reasoning for Open World Object Detection

Open World Object Detection (OWOD) is a challenging task that requires detectors to recognize known categories while discovering unlabeled objects and incrementally incorporating them as new categories. Existing methods mainly rely on known-class features to recall unknown objects, while overlooking the semantic pull of known classes in the attribute space and its impact on the interpretability of unknown detection. In this paper, we propose EviAttr-OW, a novel evidential attribute reasoning framework for OWOD that discovers potential unknown objects with interpretable attribute evidence. Specifically, we construct a class-agnostic attribute space that decouples attribute representations from known-class bias and provides a more open attribute description basis for potential unknown objects. We then map candidate-region attribute responses into a Dirichlet evidence distribution, producing known-class predictions and evidential uncertainty to measure the reliability of known-class support. Finally, we derive Known-Class Evidence Deficiency from this evidence distribution and combine it with object evidence, enabling the model to identify unknown objects with evidence-supported objectness and insufficient known-class support. Experiments on the Real-World Object Detection (RWD) benchmark across five real-world application datasets show that EviAttr-OW consistently outperforms existing state-of-the-art (SOTA) methods, achieving +7.3 mAP on unknown classes.