We present an approach that leverages multiple datasets possibly annotated using different classes to improve the semantic segmentation accuracy on each individual dataset. We propose a new selective loss function that can be integrated into deep networks to exploit training data coming from multiple datasets with possibly different tasks (e.g., different label-sets). We show how the gradient-reversal approach for domain adaptation can be used in this setup. Thorought experiments on semantic segmentation applications show the relevance of our approach.
['Damien Fourure', 'Rémi Emonet', 'Elisa Fromont', 'Damien Muselet', 'Alain Trémeau', 'Christian Wolf']
Semantic Segmentation via Multi-task, Multi-domain Learning