Detecting unseen visual relations using analogies - Archive ouverte HAL Access content directly
Conference Papers Year : 2019

Detecting unseen visual relations using analogies


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.

Dates and versions

hal-01975760 , version 1 (09-01-2019)



Julia Peyre, Ivan Laptev, Cordelia Schmid, Josef Sivic. Detecting unseen visual relations using analogies. ICCV 2019 - International Conference on Computer Vision, Oct 2019, Seoul, South Korea. pp.1981-1990, ⟨10.1109/ICCV.2019.00207⟩. ⟨hal-01975760⟩
373 View
0 Download



Gmail Mastodon Facebook X LinkedIn More