Improving Replay-Based Continual Semantic Segmentation with Smart Data Selection
This work addresses forgetting in continual semantic segmentation, an incremental improvement for computer vision applications.
The paper tackled catastrophic forgetting in continual semantic segmentation by evaluating replay strategies, finding that uniform class distribution in buffers is critical for class-incremental settings and feature-based or entropy-based sampling works best for domain-incremental settings, with effective methods reducing representation shift in early layers.
Continual learning for Semantic Segmentation (CSS) is a rapidly emerging field, in which the capabilities of the segmentation model are incrementally improved by learning new classes or new domains. A central challenge in Continual Learning is overcoming the effects of catastrophic forgetting, which refers to the sudden drop in accuracy on previously learned tasks after the model is trained on new classes or domains. In continual classification this challenge is often overcome by replaying a small selection of samples from previous tasks, however replay is rarely considered in CSS. Therefore, we investigate the influences of various replay strategies for semantic segmentation and evaluate them in class- and domain-incremental settings. Our findings suggest that in a class-incremental setting, it is critical to achieve a uniform distribution for the different classes in the buffer to avoid a bias towards newly learned classes. In the domain-incremental setting, it is most effective to select buffer samples by uniformly sampling from the distribution of learned feature representations or by choosing samples with median entropy. Finally, we observe that the effective sampling methods help to decrease the representation shift significantly in early layers, which is a major cause of forgetting in domain-incremental learning.