  The task of Video Question Answering (VideoQA) consists in answering natural
language questions about a video and serves as a proxy to evaluate the
performance of a model in scene sequence understanding. Most methods designed
for VideoQA up-to-date are end-to-end deep learning architectures which
struggle at complex temporal and causal reasoning and provide limited
transparency in reasoning steps. We present the HySTER: a Hybrid
Spatio-Temporal Event Reasoner to reason over physical events in videos. Our
model leverages the strength of deep learning methods to extract information
from video frames with the reasoning capabilities and explainability of
symbolic artificial intelligence in an answer set programming framework. We
define a method based on general temporal, causal and physics rules which can
be transferred across tasks. We apply our model to the CLEVRER dataset and
demonstrate state-of-the-art results in question answering accuracy. This work
sets the foundations for the incorporation of inductive logic programming in
the field of VideoQA.
