Quantifying Context Bias in Domain Adaptation for Object Detection

원문 초록

Domain adaptation for object detection (DAOD) seeks to transfer a trained model from a source to a target domain. Various DAOD methods exist, some of which aim to minimize context bias between foreground-background associations in various domains. However, no prior work has studied context bias in DAOD by analyzing changes in background features during adaptation and how context bias is represented in different domains. Our research experiment highlights the potential usability of context bias in DAOD. We address the problem by varying activation values over different layers of two different trained models, Detectron2 and YOLOv11, and by masking the background, both of which impact the number and quality of detections. We use two synthetic datasets, CARLA and Virtual KITTI, and two different versions of real open-source data, Cityscapes and KITTI semantic, as separate domains to represent and quantify context bias. We utilize different metrics such as Maximum Mean Discrepancy (MMD) and Maximum Variance Discrepancy (MVD) to find the layer-specific conditional probability estimates of foreground given manipulated background regions for separate domains. We further analyze foreground-background associations across various dataset combinations. We find that state-of-the-art domain adaptation methods exhibit some form of context bias and apply a potentially simple way to alleviate the context bias achieving improved accuracy (from 51.189 to 53.646 mAP on Cityscapes foggy validation with 63.207 mAP and 64.233 mAP on Cityscapes validation respectively). We demonstrate through detailed analysis that understanding of the context bias can affect DAOD approach and focusing solely on aligning foreground features is insufficient for effective DAOD.