  Manipulation actions transform objects from an initial state into a final
state. In this paper, we report on the use of object state transitions as a
mean for recognizing manipulation actions. Our method is inspired by the
intuition that object states are visually more apparent than actions from a
still frame and thus provide information that is complementary to
spatio-temporal action recognition. We start by defining a state transition
matrix that maps action labels into a pre-state and a post-state. From each
keyframe, we learn appearance models of objects and their states. Manipulation
actions can then be recognized from the state transition matrix. We report
results on the EPIC kitchen action recognition challenge.
