We seek to detect visual relations in images of the form of triplets t = (subject, predicate, object), such as 'person riding dog', where training examples of the individual entities are available but their combinations are rare or unseen at training. This is an important set-up due to the combinatorial nature of visual relations : collecting sufficient training data for all possible triplets would be very hard. The contributions of this work are three-fold. First, we learn a representation of visual relations that combines (i) individual embeddings for subject, object and predicate together with (ii) a visual phrase embedding that represents the relation triplet. Second, we learn how to transfer visual phrase embeddings from existing training triplets to unseen test triplets using analogies between relations that involve similar objects. Third, we demonstrate the benefits of our approach on two challenging datasets involving rare and unseen relations : on HICO-DET, our model achieves significant improvement over a strong baseline, and we confirm this improvement on retrieval of unseen triplets on the UnRel rare relation dataset.
Alternatively, Peyre et al. REF learn a visual relation representation combining compositional representation for subject, target and predicate with a visual phrase representation for HOI detection.
Detecting rare visual relations using analogies
ArXiv
ArXiv
Computer Science