  One key bottleneck of employing state-of-the-art semantic segmentation
networks in the real world is the availability of training labels. Conventional
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 results of state-of-the-art in more detail, we
notice that they are remarkably 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, ISLE, which employs an ensemble of the "pseudo-labels" for a given
set of different semantic 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 ISLE's individual components. An exhaustive analysis was
performed to demonstrate ISLE's effectiveness over state-of-the-art frameworks
for image-level semantic segmentation.
