SoftMatch Distance：一种新的双时图像弱监督趋势变化检测距离
SoftMatch Distance: A Novel Distance for Weakly-Supervised Trend Change Detection in Bi-Temporal Images
摘要：
一般变化检测（GCD）和语义变化检测（SCD）分别是识别变化和区分这些变化中涉及的对象类别的常用方法。然而，GCD提供的二进制变化往往不够实用，而为训练SCD模型标注语义标签的成本非常高。因此，本文提出了一种新颖的解决方案，直观地将变化分为三种趋势（“出现”、“消失”和“变换”），而不是语义类别，称为趋势变化检测（TCD）。它提供了比GCD更详细的更改信息，同时需要比SCD更少的手动注释成本。然而，有有限的公共数据集与特定的趋势标签，以支持TCD应用。为了解决这个问题，我们提出了一个软匹配距离，用于在一个简单的GCD模型中构建一个弱监督的TCD分支，使用GCD标签代替TCD标签进行训练。此外，提出了一种策略方法，成功地探索和提取背景信息，这是至关重要的弱监督TCD任务。在四个公共数据集上的实验结果非常令人鼓舞，这证明了我们提出的模型的有效性。
英文摘要：
General change detection (GCD) and semantic change detection (SCD) are common methods for identifying changes and distinguishing object categories involved in those changes, respectively. However, the binary changes provided by GCD is often not practical enough, while annotating semantic labels for training SCD models is very expensive. Therefore, there is a novel solution that intuitively dividing changes into three trends (``appear'', ``disappear'' and ``transform'') instead of semantic categories, named it trend change detection (TCD) in this paper. It offers more detailed change information than GCD, while requiring less manual annotation cost than SCD. However, there are limited public data sets with specific trend labels to support TCD application. To address this issue, we propose a softmatch distance which is used to construct a weakly-supervised TCD branch in a simple GCD model, using GCD labels instead of TCD label for training. Furthermore, a strategic approach is presented to successfully explore and extract background information, which is crucial for the weakly-supervised TCD task. The experiment results on four public data sets are highly encouraging, which demonstrates the effectiveness of our proposed model.
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。
