Weakly-supervised semantic segmentation aims to reduce labeling costs by training semantic segmentation models using weak supervision, such as image-level class labels. However, most approaches struggle to produce accurate localization maps and suffer from false predictions in class-related backgrounds (i.e., biased objects), such as detecting a railroad with the train class. Recent methods that remove biased objects require additional supervision for manually identifying biased objects for each problematic class and collecting their datasets by reviewing predictions, limiting their applicability to the real-world dataset with multiple labels and complex relationships for biasing. Following the first observation that biased features can be separated and eliminated by matching biased objects with backgrounds in the same dataset, we propose a fully-automatic/model-agnostic biased removal framework called MARS (Model-Agnostic biased object Removal without additional Supervision), which utilizes semantically consistent features of an unsupervised technique to eliminate biased objects in pseudo labels. Surprisingly, we show that MARS achieves new state-of-the-art results on two popular benchmarks, PASCAL VOC 2012 (val: 77.7%, test: 77.2%) and MS COCO 2014 (val: 49.4%), by consistently improving the performance of various WSSS models by at least 30% without additional supervision.

翻译：弱监督语义分割旨在通过使用图像级类别标签等弱监督训练语义分割模型来降低标注成本。然而，大多数方法难以生成精确定位图，并会在类别相关背景中产生虚假预测（即偏见目标），例如将铁轨错误检测为列车类别。现有移除偏见目标的方法需要额外监督，通过人工标识每个问题类别的偏见目标并审查预测结果收集数据集，限制了其在具有多标签和复杂偏见关系的真实世界数据集中的适用性。基于首个发现——通过将同一数据集中的偏见目标与背景匹配可分离并消除偏见特征——我们提出一种全自动/模型无关的偏见移除框架MARS（无需额外监督的模型无关偏见目标移除），该框架利用无监督技术的语义一致特征消除伪标签中的偏见目标。令人惊讶的是，MARS在PASCAL VOC 2012（验证集：77.7%，测试集：77.2%）和MS COCO 2014（验证集：49.4%）两个主流基准上取得了最新最优结果，无需额外监督即可使各类弱监督语义分割模型性能提升至少30%。