CCRC：面向图像变化描述与分割的变化感知描述与推理链
CCRC: A Change-Aware Captioning and Reasoning Chain for Image Change Captioning and Segmentation
AI总结：
提出变化感知描述与推理链（CCRC），通过双链框架解耦语义推理与空间分割，实现图像变化描述与分割（ICCS）任务，在合成和真实基准上达到最优性能。
摘要：
理解和定位成对图像之间的细微变化对于监控和图像编辑等任务至关重要。然而，传统的图像变化描述（ICC）方法缺乏空间定位，限制了其精度。我们引入了图像变化描述与分割（ICCS），这是一个新的多模态任务，联合要求结构化变化描述和像素级定位。为了解决ICCS，我们提出了变化感知描述与推理链（CCRC），一个双链框架，将语义推理与空间分割解耦。第一条链，变化描述链（CCC），通过基于多头变化感知注意力的视觉融合模块插入多模态大语言模型（MLLM）的视觉和语言组件之间，增强细粒度变化感知。CCC还确定变化是否可分割。如果不可分割，则仅由CCC生成描述。否则，第二条链，变化分割链（CCS）被激活，利用CCC的空间先验，并通过变化感知标记精炼器细化掩码以实现精确边界定位。我们在具有像素级监督的合成和真实世界变化检测基准上评估CCRC。实验表明CCRC达到了最先进的性能。
英文摘要：
Understanding and localizing subtle changes between paired images is critical for tasks such as surveillance and image editing. However, traditional Image Change Captioning (ICC) methods lack spatial grounding, limiting their precision. We introduce Image Change Captioning and Segmentation (ICCS), a new multimodal task that jointly requires structured change description and pixel-level localization. To address ICCS, we propose the Change-aware Captioning and Reasoning Chain (CCRC), a dual-chain framework that decouples semantic reasoning from spatial segmentation. The first chain, Chain-of-Change-Captioning (CCC), enhances fine-grained change perception via a visual fusion module based on Multi-Head Change-aware Attention inserted between the visual and language components of a Multimodal Large Language Model (MLLM). CCC also determines whether a change is segmentable. If not, it alone generates the caption. Otherwise, the second chain, Chain-of-Change-Segmenting (CCS), is activated, leveraging spatial priors from CCC and refining masks with a Change-aware Token Refiner for accurate boundary localization. We evaluate CCRC on both synthetic and real-world change detection benchmarks with pixel-level supervision. Experiments show CCRC achieves state-of-the-art performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
