  Segmentation is an integral module in many visual computing applications such
as virtual try-on, medical imaging, autonomous driving, and agricultural
automation. These applications often involve either widespread consumer use or
highly variable environments, both of which can degrade the quality of visual
sensor data, whether from a common mobile phone or an expensive satellite
imaging camera. In addition to external noises like user difference or weather
conditions, internal noises such as variations in camera quality or lens
distortion can affect the performance of segmentation models during both
development and deployment. In this work, we present an efficient, adaptable,
and gradient-free method to enhance the robustness of learning-based
segmentation models across training. First, we introduce a novel adaptive
sensitivity analysis (ASA) using Kernel Inception Distance (KID) on basis
perturbations to benchmark perturbation sensitivity of pre-trained segmentation
models. Then, we model the sensitivity curve using the adaptive SA and sample
perturbation hyperparameter values accordingly. Finally, we conduct adversarial
training with the selected perturbation values and dynamically re-evaluate
robustness during online training. Our method, implemented end-to-end with
minimal fine-tuning required, consistently outperforms state-of-the-art data
augmentation techniques for segmentation. It shows significant improvement in
both clean data evaluation and real-world adverse scenario evaluation across
various segmentation datasets used in visual computing and computer graphics
applications.
