Full Step Parameter-Efficient Continual Image Segmentation via LoRA Adaptation

In recent years, prevailing class-incremental image segmentation approaches have heavily relied on knowledge distillation—including logit distillation, feature distillation, and pseudo-labeling—which incur substantial training overhead. Meanwhile, vision segmentation models are rapidly increasing in scale; this discrepancy presents a severe dilemma, as heavy distillation paradigms fail to scale sustainably to large foundation models. In this paper, we propose a full step parameter-efficient continual image segmentation framework based on Low-Rank Adaptation (LoRA). Our approach enables lightweight and efficient adaptation of pre-trained segmentation models to novel classes from the base step through all subsequent incremental steps. Specifically, we devise a task-specific gradient subspace projection mechanism to minimize mutual interference among LoRA adapters across different steps, thereby mitigating catastrophic forgetting. Furthermore, a background classifier calibration strategy is introduced to alleviate background drift, complemented by a prototype-based loss that widens class-wise separation to suppress category confusion. Extensive experiments on ADE20K and Pascal VOC 2012 demonstrate the effectiveness of our method for semantic segmentation. In addition, we present an exploratory investigation into the applicability of LoRA fine-tuning for panoptic segmentation.