  We propose a new approach for Zero-Shot Human-Object Interaction Recognition
in the challenging setting that involves interactions with unseen actions (as
opposed to just unseen combinations of seen actions and objects). Our approach
makes use of knowledge external to the image content in the form of a graph
that models affordance relations between actions and objects, i.e., whether an
action can be performed on the given object or not. We propose a loss function
with the aim of distilling the knowledge contained in the graph into the model,
while also using the graph to regularise learnt representations by imposing a
local structure on the latent space. We evaluate our approach on several
datasets (including the popular HICO and HICO-DET) and show that it outperforms
the current state of the art.
