Instance-Warp: Saliency Guided Image Warping for Unsupervised Domain Adaptation

Shen Zheng, Anurag Ghosh, Srinivasa Narasimhan; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 8186-8195

Driving is challenging in conditions like night rain and snow. Lack of good labeled datasets has hampered progress in scene understanding under such conditions. Unsupervised Domain Adaptation (UDA) using large labeled clear-day datasets is a promising research direction in such cases. However many UDA methods are trained with dominant scene backgrounds (e.g. roads sky sidewalks) that appear dramatically different across domains. As a result they struggle to learn effective features of smaller and often sparse foreground objects (e.g. people vehicles signs). In this work we improve UDA training by applying in-place image warping to focus on salient objects. We design instance-level saliency guidance to adaptively oversample object regions and undersample background areas which reduces adverse effects from background context and enhances backbone feature learning. Our approach improves adaptation across geographies lighting and weather conditions and is agnostic to the task (segmentation detection) domain adaptation algorithm saliency guidance and underlying model architecture. Result highlights include +6.1 mAP50 for BDD100K Clear to DENSE Foggy +3.7 mAP50 for BDD100K Day to Night +3.0 mAP50 for BDD100K Clear to Rainy and +6.3 mIoU for Cityscapes to ACDC. Besides Our method adds minimal training memory and no additional inference latency. Code is available at https://github.com/ShenZheng2000/Instance-Warp.