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There have been remarkable improvements in the semantic labelling task in therecent years. However, the state of the art methods rely on large-scalepixel-level annotations. This paper studies the problem of training apixel-wise semantic labeller network from image-level annotations of thepresent object classes. Recently, it has been shown that high quality seedsindicating discriminative object regions can be obtained from image-levellabels. Without additional information, obtaining the full extent of the objectis an inherently ill-posed problem due to co-occurrences. We propose using asaliency model as additional information and hereby exploit prior knowledge onthe object extent and image statistics. We show how to combine both informationsources in order to recover 80% of the fully supervised performance - which isthe new state of the art in weakly supervised training for pixel-wise semanticlabelling. The code is available at https://goo.gl/KygSeb.

Exploiting saliency for object segmentation from image level labels