Open-vocabulary object detection (OVOD) aims to detect both seen and unseen categories, yet existing methods often struggle to generalize to novel objects due to limited integration of global and local contextual cues. We propose DetRefiner, a simple yet effective plug-and-play framework that learns to fuse global and local features to refine open-vocabulary detection. DetRefiner processes global image features and patch-level image features from foundational models (e.g., DINOv3) through a lightweight Transformer encoder. The encoder produces a class vector capturing image-level attributes and patch vectors representing local region attributes, from which attribute reliability is inferred to recalibrate the base model's confidence. Notably, DetRefiner is trained independently of the base OVOD model, requiring neither access to its internal features nor retraining. At inference, it operates solely on the base detector's predictions, producing auxiliary calibration scores that are merged with the base detector's scores to yield the final refined confidence. Despite this simplicity, DetRefiner consistently enhances multiple OVOD models across COCO, LVIS, ODinW13, and Pascal VOC, achieving gains of up to +10.1 AP on novel categories. These results highlight that learning to fuse global and local representations offers a powerful and general mechanism for advancing open-world object detection. Our codes and models are available at https://github.com/hitachi-rd-cv/detrefiner.

翻译：开放词汇目标检测（OVOD）旨在检测可见与未见类别，然而现有方法由于对全局与局部上下文线索的整合有限，常难泛化至新物体。本文提出DetRefiner——一个简单而有效的即插即用框架，通过学习融合全局与局部特征来精化开放词汇检测。DetRefiner通过轻量级Transformer编码器处理来自基础模型（如DINOv3）的全局图像特征与图像块级特征。该编码器生成捕获图像级属性的类别向量和表征局部区域属性的图像块向量，并据此推断属性可靠性以重新校准基础模型的置信度。值得注意的是，DetRefiner独立于基础OVOD模型训练，无需访问其内部特征或重新训练。推理时，它仅基于基础检测器的预测结果，生成辅助校准分数并与基础检测器的分数合并，从而得出最终精化置信度。尽管结构简单，DetRefiner在COCO、LVIS、ODinW13和Pascal VOC数据集上持续提升多种OVOD模型性能，在新类别上实现高达+10.1 AP的提升。这些结果表明，学习融合全局与局部表示为推进开放世界目标检测提供了一种强大且通用的机制。我们的代码与模型已发布于https://github.com/hitachi-rd-cv/detrefiner。