用扩散模型生成可灵活控制变化事件的多时相变化检测数据
ChangeDiff: A Multi-Temporal Change Detection Data Generator with Flexible Text Prompts via Diffusion Model
- 通过文本提示+布局生成+图像转换三步法生成变化数据
- 支持可控类别与比例，生成数据在时空连续性上表现优异
- 适合需要多样化标注数据的研究者快速构建训练集
数据驱动的深度学习模型在变化检测（CD）任务中取得显著进展，但像素级标注数据收集成本高、耗时长。现有生成方法仍存在难以灵活控制变化事件、依赖额外训练数据、任务专一等问题。本文聚焦语义变化检测（SCD），提出基于扩散模型的多时相SCD数据生成器ChangeDiff。该方法分两步：首先利用文本提示和文本到布局（T2L）模型生成连续布局；再通过布局到图像（L2I）模型生成图像。提出多类分布引导文本提示（MCDG-TP），支持通过可控类别及其比例灵活生成布局；并设计类别分布精修损失以适配T2L模型。MCDG-TP支持三种模式，可从不同文本生成新布局掩码。生成数据在时序连续性、空间多样性与真实感方面均有显著提升，有效增强变化检测器的准确率与迁移能力。代码已开源。
原文摘要 · Abstract (English)
Data-driven deep learning models have enabled tremendous progress in change detection (CD) with the support of pixel-level annotations. However, collecting diverse data and manually annotating them is costly, laborious, and knowledge-intensive. Existing generative methods for CD data synthesis show competitive potential in addressing this issue but still face the following limitations: 1) difficulty in flexibly controlling change events, 2) dependence on additional data to train the data generators, 3) focus on specific change detection tasks. To this end, this paper focuses on the semantic CD (SCD) task and develops a multi-temporal SCD data generator ChangeDiff by exploring powerful diffusion models. ChangeDiff innovatively generates change data in two steps: first, it uses text prompts and a text-to-layout (T2L) model to create continuous layouts, and then it employs layout-to-image (L2I) to convert these layouts into images. Specifically, we propose multi-class distribution-guided text prompts (MCDG-TP), allowing for layouts to be generated flexibly through controllable classes and their corresponding ratios. Subsequently, to generalize the T2L model to the proposed MCDG-TP, a class distribution refinement loss is further designed as training supervision. %For the former, a multi-classdistribution-guided text prompt (MCDG-TP) is proposed to complement via controllable classes and ratios. To generalize the text-to-image diffusion model to the proposed MCDG-TP, a class distribution refinement loss is designed as training supervision. For the latter, MCDG-TP in three modes is proposed to synthesize new layout masks from various texts. Our generated data shows significant progress in temporal continuity, spatial diversity, and quality realism, empowering change detectors with accuracy and transferability. The code is available at https://github.com/DZhaoXd/ChangeDiff
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