Research on anti-forgetting training strategies for open-vocabulary object detection systems
Open-vocabulary object detection leverages vision-language pre-training to achieve cross-category generalization. With the progressive refinement of models such as GLIP, Grounding DINO, and YOLO-World, detectors have exhibited substantially enhanced zero-shot recognition capabilities on open vocabularies, with YOLO-World further advancing this capability into an efficient single-stage detection paradigm. However, during scenario-specific fine-tuning, training that relies solely on ground-truth annotations of base classes induces rapid degradation in the model’s discriminative ability for novel classes, often reverting to closed-set detector behavior. To address this issue, the present paper proposes a forgetting-resistant fine-tuning framework that decouples the training objectives into strong supervised adaptation for base classes and weak supervised preservation for novel classes. Specifically, novel pseudo-boxes are generated once by a frozen zero-shot teacher before any task-specific fine-tuning and are refined through threshold filtering, score weighting, and soft-ignore mechanisms to reduce the impact of noisy pseudo-supervision. Subsequently, teacher-guided logit distillation is incorporated, constraining the student model exclusively at the output distribution level to preserve its response relationships to the novel vocabulary. Experimental results demonstrate that conventional fine-tuning on YOLO-World-v2-S reduces Novel AP50 from 57.6 to 1.6. In contrast, through stepwise integration of the freezing strategy, pseudo-labels, and logit distillation, the proposed method elevates Novel AP50 to 51.6 and achieves an All AP50 of 53.7. These findings show that the proposed approach substantially mitigates catastrophic forgetting while preserving the original single-stage inference pipeline.