# Detecting unseen visual relations using analogies

## TL;DR

This paper introduces a method for detecting unseen visual relations in images by learning relation representations and transferring knowledge via analogies, significantly improving performance on multiple datasets.

## Contribution

It proposes a novel approach combining individual and phrase embeddings and transferring relations through analogies, addressing the challenge of unseen triplet detection.

## Key findings

- Significant improvement on HICO-DET for both frequent and unseen triplets.
- Enhanced retrieval of unseen triplets with out-of-vocabulary predicates on COCO-a.
- Effective detection of unusual triplets in the UnRel dataset.

## Abstract

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 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 three challenging datasets : on HICO-DET, our model achieves significant improvement over a strong baseline for both frequent and unseen triplets, and we observe similar improvement for the retrieval of unseen triplets with out-of-vocabulary predicates on the COCO-a dataset as well as the challenging unusual triplets in the UnRel dataset.