  Image semantic segmentation is parsing image into several partitions in such
a way that each region of which involves a semantic concept. In a weakly
supervised manner, since only image-level labels are available, discriminating
objects from the background is challenging, and in some cases, much more
difficult. More specifically, some objects which are commonly seen in one
specific scene (e.g. 'train' typically is seen on 'railroad track') are much
more likely to be confused. In this paper, we propose a method to add the
target-specific scenes in order to overcome the aforementioned problem.
Actually, we propose a scene recommender which suggests to add some specific
scene contexts to the target dataset in order to train the model more
accurately. It is notable that this idea could be a complementary part of the
baselines of many other methods. The experiments validate the effectiveness of
the proposed method for the objects for which the scene context is added.
