Spatial–Adaptive Replay for Foreground Classes in Class-Incremental Semantic Segmentation

Class-Incremental Semantic Segmentation (CISS) addresses the challenge of catastrophic forgetting in semantic segmentation models. In autonomous driving scenarios, the model can learn the background class information from the new data due to the repetition of many structural background classes in new data. Traditional replay-based methods store the original pixels of these background classes from old data, resulting in low memory efficiency. To enhance memory efficiency, we propose Spatial–Adaptive replay for Foreground objects (SAF), a method that stores only foreground-class pixels and their spatial information. In addition, Spatial–Adaptive Mix-up (SAM) is designed in the proposed method, based on the spatial distribution characteristics of the foreground classes. Spatial–adaptive alignment ensures that foreground objects are mixed with new samples in a reasonable spatial arrangement to help the model learn critical contextual information. Experiments on the Cityscapes and BDD100K datasets show that the proposed method obtains competitive results in class-incremental semantic segmentation tasks for autonomous driving scenarios.