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The main obstacle to weakly supervised semantic image segmentation is thedifficulty of obtaining pixel-level information from coarse image-levelannotations. Most methods based on image-level annotations use localizationmaps obtained from the classifier, but these only focus on the smalldiscriminative parts of objects and do not capture precise boundaries.FickleNet explores diverse combinations of locations on feature maps created bygeneric deep neural networks. It selects hidden units randomly and then usesthem to obtain activation scores for image classification. FickleNet implicitlylearns the coherence of each location in the feature maps, resulting in alocalization map which identifies both discriminative and other parts ofobjects. The ensemble effects are obtained from a single network by selectingrandom hidden unit pairs, which means that a variety of localization maps aregenerated from a single image. Our approach does not require any additionaltraining steps and only adds a simple layer to a standard convolutional neuralnetwork; nevertheless it outperforms recent comparable techniques on the PascalVOC 2012 benchmark in both weakly and semi-supervised settings.

FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference