Continual Semantic Segmentation via Scalable Contrastive Clustering and Background Diversity

Despite the efficacy towards static data distribution, traditional semantic segmentation methods encounter Catastrophic forgetting when tackling continually changing data streams. Another fundamental challenge is Background shift, which results from the semantic drift of the background class during continual learning steps. To extend the applicability of semantic segmentation methods, we introduce a novel, scalable segmentation architecture called ScaleSeg, designed to adapt the incremental scenarios. The architecture of ScaleSeg consists of a series of prototypes updated by online contrastive clustering. Additionally, we propose a background diversity strategy to enhance the model’s plasticity and stability, thus overcoming background shift. Comprehensive experiments and ablation studies on challenging benchmarks demonstrate that ScaleSeg surpasses previous state-of-the-art methods, particularly when dealing with extensive task sequences.