VCL Challenges 2023 at ICCV 2023 Technical Report: Bi-level Adaptation
  Method for Test-time Adaptive Object Detection
Chenyu Lin Yusheng He Zhengqing Zang Chenwei Tang Tao Wang Jiancheng Lv
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.