  We study in this paper the problem of novel human-object interaction (HOI)
detection, aiming at improving the generalization ability of the model to
unseen scenarios. The challenge mainly stems from the large compositional space
of objects and predicates, which leads to the lack of sufficient training data
for all the object-predicate combinations. As a result, most existing HOI
methods heavily rely on object priors and can hardly generalize to unseen
combinations. To tackle this problem, we propose a unified framework of
adversarial domain generalization to learn object-invariant features for
predicate prediction. To measure the performance improvement, we create a new
split of the HICO-DET dataset, where the HOIs in the test set are all unseen
triplet categories in the training set. Our experiments show that the proposed
framework significantly increases the performance by up to 50% on the new split
of HICO-DET dataset and up to 125% on the UnRel dataset for auxiliary
evaluation in detecting novel HOIs.
