Go to ICLR 2025 Conference homepageSelective LoRA for Domain-Aligned Dataset Generation in Urban-Scene Segmentation
This paper addresses the challenge of data scarcity in semantic segmentation by generating datasets through fine-tuned text-to-image generation models, reducing the costs of image acquisition and labeling. Segmentation dataset generation faces two key challenges: 1) aligning generated samples with the target domain and 2) producing informative samples beyond the training data. Existing methods often overfit and memorize training data, limiting their ability to generate diverse and well-aligned samples. To overcome these issues, we propose Selective LoRA, a novel fine-tuning approach that selectively identifies and updates only the weights associated with necessary concepts (e.g., style or viewpoint) for domain alignment while leveraging the pretrained knowledge of the image generation model to produce more informative samples. Our approach ensures effective domain alignment and enhances sample diversity. We demonstrate its effectiveness in generating datasets for urban-scene segmentation, outperforming baseline and state-of-the-art methods in in-domain (few-shot and fully-supervised) settings, as well as domain generalization tasks, especially under challenging conditions such as adverse weather and varying illumination, further highlighting its superiority.