  Action recognition in still images has seen major improvement in recent years
due to advances in human pose estimation, object recognition and stronger
feature representations produced by deep neural networks. However, there are
still many cases in which performance remains far from that of humans. A major
difficulty arises in distinguishing between transitive actions in which the
overall actor pose is similar, and recognition therefore depends on details of
the grasp and the object, which may be largely occluded. In this paper we
demonstrate how recognition is improved by obtaining precise localization of
the action-object and consequently extracting details of the object shape
together with the actor-object interaction. To obtain exact localization of the
action object and its interaction with the actor, we employ a coarse-to-fine
approach which combines semantic segmentation and contextual features, in
successive stages. We focus on (but are not limited) to face-related actions, a
set of actions that includes several currently challenging categories. We
present an average relative improvement of 35% over state-of-the art and
validate through experimentation the effectiveness of our approach.
