DR.WILSS: Diffusion-Based Replay for Weakly Supervised Continual Semantic Segmentation

2026-09-16

AI Summary

本文提出DR.WILSS方法，利用基于扩散的生成重放解决弱监督持续语义分割中的灾难性遗忘问题，通过自修复和正则化技术高效生成重放数据。

Abstract

Weakly supervised class-incremental semantic segmentation (WILSS) aims to train a segmentation model over multiple steps, each introducing new concepts to be learned with only image-level supervision. We introduce DR.WILSS, an innovative approach to address catastrophic forgetting in continual learning using diffusion-based generative replay. Our framework leverages language clues to guide the diffusion process, employing self-inpainting and regularization techniques to efficiently produce replay data, aiding the learning process. By generating high-quality replay data, the information from previously learned classes can be preserved during continual updates, a critical challenge in incremental learning scenarios. To further align the statistics of replay data with those of training samples, we apply LoRAs to the generative model. Experimental results demonstrate state-of-the-art performance across multiple benchmarks and generative architectures, while avoiding storage of training data and the use of additional resource-demanding tools during training. The proposed technique enables an optimal tradeoff between training complexity and inference-time accuracy, making DR.WILSS a promising solution for real-world applications.

Problem

• Weakly Supervised
• Continual Learning
• Catastrophic Forgetting
• Semantic Segmentation

Innovation

• diffusion-based generative replay
• language clues
• self-inpainting
• LoRAs