Align and Distill: Unifying and Improving Domain Adaptive Object
Detection
Align and Distill: Unifying and Improving Domain Adaptive Object
Detection
Object detectors often perform poorly on data that differs from their training set. Domain adaptive object detection (DAOD) methods have recently demonstrated strong results on addressing this challenge. Unfortunately, we identify systemic benchmarking pitfalls that call past results into question and hamper further progress: (a) Overestimation of performance due to …