  Important high-level vision tasks such as human-object interaction, image
captioning and robotic manipulation require rich semantic descriptions of
objects at part level. Based upon previous work on part localization, in this
paper, we address the problem of inferring rich semantics imparted by an object
part in still images. We propose to tokenize the semantic space as a discrete
set of part states. Our modeling of part state is spatially localized,
therefore, we formulate the part state inference problem as a pixel-wise
annotation problem. An iterative part-state inference neural network is
specifically designed for this task, which is efficient in time and accurate in
performance. Extensive experiments demonstrate that the proposed method can
effectively predict the semantic states of parts and simultaneously correct
localization errors, thus benefiting a few visual understanding applications.
The other contribution of this paper is our part state dataset which contains
rich part-level semantic annotations.
