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：在现实世界中,使用最先进的语义分解网络的一个关键瓶颈是培训标签的可用性。标准语义分解网络需要大量的像素分解标签,以达到最先进的预测质量。因此,一些工作的重点是只用图像级别注释来训练的语义分解网络。然而,当对最新语义分解网络进行更细致的审查时,我们注意到,虽然在平均预测质量方面它们彼此非常接近,但不同方法在不同类别中表现更好,而在其他类别中则提供低质量。为解决这一问题,我们提议了一个新颖的框架,即AutoEngble,它使用“假语-标签”的组合来达到最先进的预测质量。我们用AutoEmple-lable来显示一个等级层次上不同分解技术的一套特定组合。Pseudo-lables是用于培训最后分解模型的像级框架的精度预测。我们的伪语义标签无缝地结合了多个分解技术的强点,以达到高级预测质量。我们用Aute-Eng-Annex imal imal imal ims ass real ass ass laction to laud lauds totototototototototototototost atost flaudation</s>