Data Factory with Minimal Human Effort Using VLMs

• URL: http://arxiv.org/abs/2510.05722v1
• Date: Tue, 07 Oct 2025 09:43:24 GMT
• Title: Data Factory with Minimal Human Effort Using VLMs
• Authors: Jiaojiao Ye, Jiaxing Zhong, Qian Xie, Yuzhou Zhou, Niki Trigoni, Andrew Markham,
• Abstract summary: We introduce a training-free pipeline that integrates pretrained ControlNet and Vision-Language Models (VLMs) to generate synthetic images paired with pixel-level labels.<n>This approach eliminates the need for manual annotations and significantly improves downstream tasks.<n>Our results on PASCAL-5i and COCO-20i present promising performance and outperform concurrent work for one-shot semantic segmentation.
• Score: 35.30747487237989
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating enough and diverse data through augmentation offers an efficient solution to the time-consuming and labour-intensive process of collecting and annotating pixel-wise images. Traditional data augmentation techniques often face challenges in manipulating high-level semantic attributes, such as materials and textures. In contrast, diffusion models offer a robust alternative, by effectively utilizing text-to-image or image-to-image transformation. However, existing diffusion-based methods are either computationally expensive or compromise on performance. To address this issue, we introduce a novel training-free pipeline that integrates pretrained ControlNet and Vision-Language Models (VLMs) to generate synthetic images paired with pixel-level labels. This approach eliminates the need for manual annotations and significantly improves downstream tasks. To improve the fidelity and diversity, we add a Multi-way Prompt Generator, Mask Generator and High-quality Image Selection module. Our results on PASCAL-5i and COCO-20i present promising performance and outperform concurrent work for one-shot semantic segmentation.

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