Leveraging Knowledge Graphs for Zero-Shot Object-agnostic State
  Classification
Filipos Gouidis Theodore Patkos Antonis Argyros Dimitris Plexousakis
We investigate the problem of Object State Classification (OSC) as a
zero-shot learning problem. Specifically, we propose the first Object-agnostic
State Classification (OaSC) method that infers the state of a certain object
without relying on the knowledge or the estimation of the object class. In that
direction, we capitalize on Knowledge Graphs (KGs) for structuring and
organizing knowledge, which, in combination with visual information, enable the
inference of the states of objects in object/state pairs that have not been
encountered in the method's training set. A series of experiments investigate
the performance of the proposed method in various settings, against several
hypotheses and in comparison with state of the art approaches for object
attribute classification. The experimental results demonstrate that the
knowledge of an object class is not decisive for the prediction of its state.
Moreover, the proposed OaSC method outperforms existing methods in all datasets
and benchmarks by a great margin.