  Localizing functional regions of objects or affordances is an important
aspect of scene understanding. In this work, we cast the problem of affordance
segmentation as that of semantic image segmentation. In order to explore
various levels of supervision, we introduce a pixel-annotated affordance
dataset of 3090 images containing 9916 object instances with rich contextual
information in terms of human-object interactions. We use a deep convolutional
neural network within an expectation maximization framework to take advantage
of weakly labeled data like image level annotations or keypoint annotations. We
show that a further reduction in supervision is possible with a minimal loss in
performance when human pose is used as context.
