Beyond Holistic Object Recognition:Enriching Image Understanding with Part States
Abstract
Important high-level vision tasks 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. Specifically, 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 that is efficient in time and accurate in performance
is specifically designed for this task. Extensive experiments
demonstrate that the proposed method can effectively predict the semantic states of parts and simultaneously improve
part segmentation, thus benefiting a number of visual understanding applications. The other contribution of this paper is our part state dataset which contains rich part-level
semantic annotations