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.

翻译：通用变化检测（GCD）与语义变化检测（SCD）分别用于识别变化及区分所涉及对象的语义类别。然而，GCD提供的二元变化信息往往不够实用，而为训练SCD模型标注语义标签成本高昂。为此，本文提出一种创新解决方案——将变化直观划分为三种趋势（“出现”、“消失”与“转换”），称之为趋势变化检测（TCD）。该方法比GCD提供更详尽的变化信息，同时比SCD所需的人工标注成本更低。然而，目前支持TCD应用的专用趋势标签公开数据集十分有限。针对该问题，我们提出一种软匹配距离，用于在简易GCD模型中构建弱监督TCD分支，从而利用GCD标签替代TCD标签进行训练。此外，本文提出一种策略性方法以有效探索并提取背景信息，这对于弱监督TCD任务至关重要。在四个公开数据集上的实验结果令人振奋，充分证明了所提模型的有效性。