Deep convolutional neural networks are one of the most powerful algorithms for understanding image content. Among their achievements, these networks have shown to be very effective in addressing the semantic segmentation problem, the goal of which is to assign the correct semantic label to each pixel in the image. However, having a full pixel-wise annotation to train these models in a supervised way is unlikely in the reality, both because of the burden of the pixel-wise annotation process and the high variety of existing concepts that cannot be captured by a single training set. To sidestep these issues, researchers have explored the Generalized Zero-Shot Learning (GZSL) scenario, that is building models able to assign pixels to both training classes (seen) and classes with no labeled pixels available at training time (unseen), using side-information capturing seen and unseen classes relationships in a given semantic space.