Addressing Source Scale Bias via Image Warping for Domain Adaptation
Shen Zheng Anurag Ghosh Srinivasa G. Narasimhan
In visual recognition, scale bias is a key challenge due to the imbalance of
object and image size distribution inherent in real scene datasets.
Conventional solutions involve injecting scale invariance priors, oversampling
the dataset at different scales during training, or adjusting scale at
inference. While these strategies mitigate scale bias to some extent, their
ability to adapt across diverse datasets is limited. Besides, they increase
computational load during training and latency during inference. In this work,
we use adaptive attentional processing -- oversampling salient object regions
by warping images in-place during training. Discovering that shifting the
source scale distribution improves backbone features, we developed a
instance-level warping guidance aimed at object region sampling to mitigate
source scale bias in domain adaptation. Our approach improves adaptation across
geographies, lighting and weather conditions, is agnostic to the task, domain
adaptation algorithm, saliency guidance, and underlying model architecture.
Highlights include +6.1 mAP50 for BDD100K Clear $\rightarrow$ DENSE Foggy, +3.7
mAP50 for BDD100K Day $\rightarrow$ Night, +3.0 mAP50 for BDD100K Clear
$\rightarrow$ Rainy, and +6.3 mIoU for Cityscapes $\rightarrow$ ACDC. Our
approach adds minimal memory during training and has no additional latency at
inference time. Please see Appendix for more results and analysis.