  Generating training sets for deep convolutional neural networks (DCNNs) is a
bottleneck for modern real-world applications. This is a demanding task for
applications where annotating training data is costly, such as in semantic
segmentation. In the literature, there is still a gap between the performance
achieved by a network trained on full and on weak annotations. In this paper,
we establish a strategy to measure this gap and to identify the ingredients
necessary to reduce it.
  On scribbles, we establish new state-of-the-art results: we obtain a mIoU of
75.6% without, and 75.7% with CRF post-processing. We reduce the gap by 64.2%
whereas the current state-of-the-art reduces it only by 57.5%. Thanks to a
systematic study of the different ingredients involved in the weakly supervised
scenario and an original experimental strategy, we unravel a counter-intuitive
mechanism that is simple and amenable to generalisations to other
weakly-supervised scenarios: averaging poor local predicted annotations with
the baseline ones and reuse them for training a DCNN yields new
state-of-the-art results.
