This report outlines our team's participation in VCL Challenges B Continual Test_time Adaptation, focusing on the technical details of our approach. Our primary focus is Testtime Adaptation using bi_level adaptations, encompassing image_level and detector_level adaptations. At the image level, we employ adjustable parameterbased image filters, while at the detector level, we leverage adjustable parameterbased mean teacher modules. Ultimately, through the utilization of these bi_level adaptations, we have achieved a remarkable 38.3% mAP on the target domain of the test set within VCL Challenges B. It is worth noting that the minimal drop in mAP, is mearly 4.2%, and the overall performance is 32.5% mAP.

翻译：摘要：本报告概述了我们团队参与VCL挑战赛B连续测试时自适应任务的方案，重点阐述所采用方法的技术细节。我们的核心贡献在于通过双层自适应机制实现测试时自适应，该机制包含图像级与检测器级两类自适应策略。在图像层级，我们采用了基于可调参数的图像滤波器；在检测器层级，则运用了基于可调参数的均值教师模块。最终，通过实施这种双层自适应方案，我们在VCL挑战赛B测试集目标域上取得了38.3%的mAP（平均精度均值）。值得注意的是，mAP的最小衰减幅度仅为4.2%，整体性能达到了32.5% mAP。