提出开放词汇变化检测，无需预定义类别即可识别任意地物变化。
DynamicEarth: How Far are We from Open-Vocabulary Change Detection?
- 利用现成大模型构建无训练框架，先发现变化再分类或先定位目标再判断状态变化。
- 在5个基准数据集上表现优于现有监督与无监督方法，泛化能力更强。
- 适合遥感、环境监测等需动态感知新类别的开放世界应用。
监测地球地表覆盖的动态变化需要能够跨多种类别和场景检测变化的方法。现有变化检测方法受限于预定义类别的依赖，在开放世界应用中效果不佳。为此，我们提出开放词汇变化检测（OVCD）这一新任务，通过融合视觉与语言能力，实现对任意类别的变化检测。针对高质量数据与标注匮乏的问题，我们设计了两种无需训练的框架：M-C-I（先发现所有潜在变化再分类）和I-M-C（先识别关注目标再判断状态是否改变）。基于此，我们构建了多个方法，如SAM-DINOv2-SegEarth-OV、Grounding-DINO-SAM2-DINO等。在5个基准数据集上的广泛评估表明，我们的方法在泛化性和鲁棒性方面均优于现有监督与无监督方法。为支持持续研究，我们发布了DynamicEarth代码库，旨在推动OVCD的研究与应用。
原文摘要 · Abstract (English)
Monitoring Earth's evolving land covers requires methods capable of detecting changes across a wide range of categories and contexts. Existing change detection methods are hindered by their dependency on predefined classes, reducing their effectiveness in open-world applications. To address this issue, we introduce open-vocabulary change detection (OVCD), a novel task that bridges vision and language to detect changes across any category. Considering the lack of high-quality data and annotation, we propose two training-free frameworks, M-C-I and I-M-C, which leverage and integrate off-the-shelf foundation models for the OVCD task. The insight behind the M-C-I framework is to discover all potential changes and then classify these changes, while the insight of I-M-C framework is to identify all targets of interest and then determine whether their states have changed. Based on these two frameworks, we instantiate to obtain several methods, e.g., SAM-DINOv2-SegEarth-OV, Grounding-DINO-SAM2-DINO, etc. Extensive evaluations on 5 benchmark datasets demonstrate the superior generalization and robustness of our OVCD methods over existing supervised and unsupervised methods. To support continued exploration, we release DynamicEarth, a dedicated codebase designed to advance research and application of OVCD. https://likyoo.github.io/DynamicEarth
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