A key bottleneck of employing state-of-the-art semantic segmentation networks in the real world is the availability of training labels. Standard semantic segmentation networks require massive pixel-wise annotated labels to reach state-of-the-art prediction quality. Hence, several works focus on semantic segmentation networks trained with only image-level annotations. However, when scrutinizing the state-of-the-art results in more detail, we notice that although they are very close to each other on average prediction quality, different approaches perform better in different classes while providing low quality in others. To address this problem, we propose a novel framework, AutoEnsemble, which employs an ensemble of the "pseudo-labels" for a given set of different segmentation techniques on a class-wise level. Pseudo-labels are the pixel-wise predictions of the image-level semantic segmentation frameworks used to train the final segmentation model. Our pseudo-labels seamlessly combine the strong points of multiple segmentation techniques approaches to reach superior prediction quality. We reach up to 2.4% improvement over AutoEnsemble's components. An exhaustive analysis was performed to demonstrate AutoEnsemble's effectiveness over state-of-the-art frameworks for image-level semantic segmentation.

翻译：在真实场景中应用最先进的语义分割网络的一个关键瓶颈是训练标签的可用性。标准的语义分割网络需要大量像素级标注标签才能达到最先进的预测质量。因此，多项研究聚焦于仅使用图像级标注训练的语义分割网络。然而，当更细致地审视当前最优结果时，我们注意到尽管它们在平均预测质量上非常接近，但不同方法在不同类别上表现更优，而在其他类别上质量较低。为解决这一问题，我们提出了一种新颖框架AutoEnsemble，该框架在类别级别上对给定的一组不同分割技术生成的"伪标签"进行集成。伪标签是图像级语义分割框架生成的像素级预测，用于训练最终的分割模型。我们的伪标签无缝融合了多种分割技术方法的优势，以实现更优的预测质量。相较于AutoEnsemble的各个组件，我们实现了最高2.4%的提升。通过详尽的分析，我们证明了AutoEnsemble在图像级语义分割领域相较于当前最优框架的有效性。