Weakly Incremental Learning for Semantic Segmentation (WILSS) leverages a pre-trained segmentation model to segment new classes using cost-effective and readily available image-level labels. A prevailing way to solve WILSS is the generation of seed areas for each new class, serving as a form of pixel-level supervision. However, a scenario usually arises where a pixel is concurrently predicted as an old class by the pre-trained segmentation model and a new class by the seed areas. Such a scenario becomes particularly problematic in WILSS, as the lack of pixel-level annotations on new classes makes it intractable to ascertain whether the pixel pertains to the new class or not. To surmount this issue, we propose an innovative, tendency-driven relationship of mutual exclusivity, meticulously tailored to govern the behavior of the seed areas and the predictions generated by the pre-trained segmentation model. This relationship stipulates that predictions for the new and old classes must not conflict whilst prioritizing the preservation of predictions for the old classes, which not only addresses the conflicting prediction issue but also effectively mitigates the inherent challenge of incremental learning - catastrophic forgetting. Furthermore, under the auspices of this tendency-driven mutual exclusivity relationship, we generate pseudo masks for the new classes, allowing for concurrent execution with model parameter updating via the resolution of a bi-level optimization problem. Extensive experiments substantiate the effectiveness of our framework, resulting in the establishment of new benchmarks and paving the way for further research in this field.

翻译：弱增量式语义分割学习（WILSS）利用预训练的分割模型，通过成本低廉且易于获取的图像级标签对新类别进行分割。解决WILSS的主流方法是为每个新类别生成种子区域，作为像素级监督的一种形式。然而，常出现这样一种场景：预训练模型将某个像素预测为旧类别，而种子区域将其预测为新类别。这种场景在WILSS中尤为棘手，因为新类别缺乏像素级标注，导致难以确定该像素是否属于新类别。为解决此问题，我们提出一种创新的、倾向驱动的互斥关系，专门用于规范种子区域与预训练模型预测结果的行为。该关系要求新旧类别的预测不得冲突，同时优先保留旧类别的预测，这不仅解决了预测冲突问题，还有效缓解了增量学习的内在挑战——灾难性遗忘。此外，在此倾向驱动互斥关系的支持下，我们为新类别生成伪掩码，并通过求解双层优化问题实现与模型参数更新的同步执行。大量实验证明了我们框架的有效性，从而建立了新的基准，并为该领域的进一步研究铺平了道路。