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

arXiv:2609.18444 · cs.CV · Submitted 2026-09-16 · Read on arXiv

cs.CV

Submitted: 2026-09-16

Updated: 2026-09-16

Comments: Accepted at MMSP 2026, 6 pages, 4 figures

Code: https://github.com/LTTM/DRWILSS

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Semantic Segmentation — 22× in this paper
• Diffusion Model — 13× in this paper · explained in 8 episodes
• Diffusion Models — 9× in this paper · explained in 6 episodes
• Continual Learning — 7× in this paper · explained in 2 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Catastrophic Forgetting — 4× in this paper · explained in 5 episodes
• Distillation — 4× in this paper · explained in 2 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Knowledge Distillation — 2× in this paper · explained in 11 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• LoRA Adapters — 2× in this paper · explained in 3 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Maximum Mean Discrepancy — 1× in this paper
• Personalization — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• T-LoRA: Single Image Diffusion Model Customization Without Overfitting

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