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In this paper, we attempt to specialize the VLM model for OWOD tasks bydistilling its open-world knowledge into a language-agnostic detector.Surprisingly, we observe that the combination of a simple \textbf{knowledgedistillation} approach and the automatic pseudo-labeling mechanism in OWOD canachieve better performance for unknown object detection, even with a smallamount of data. Unfortunately, knowledge distillation for unknown objectsseverely affects the learning of detectors with conventional structures forknown objects, leading to catastrophic forgetting. To alleviate these problems,we propose the \textbf{down-weight loss function} for knowledge distillationfrom vision-language to single vision modality. Meanwhile, we propose the\textbf{cascade decouple decoding structure} that decouples the learning oflocalization and recognition to reduce the impact of category interactions ofknown and unknown objects on the localization learning process. Ablationexperiments demonstrate that both of them are effective in mitigating theimpact of open-world knowledge distillation on the learning of known objects.Additionally, to alleviate the current lack of comprehensive benchmarks forevaluating the ability of the open-world detector to detect unknown objects inthe open world, we propose two benchmarks, which we name"\textbf{StandardSet}$\heartsuit$" and "\textbf{IntensiveSet}$\spadesuit$"respectively, based on the complexity of their testing scenarios. Comprehensiveexperiments performed on OWOD, MS-COCO, and our proposed benchmarks demonstratethe effectiveness of our methods. The code and proposed dataset are availableat \url{https://github.com/xiaomabufei/SKDF}.

SKDF: A Simple Knowledge Distillation Framework for Distilling Open-Vocabulary Knowledge to Open-world Object Detector