Deeply Supervised Multimodal Attentional Translation Embeddings for Visual Relationship Detection
Abstract
Detecting visual relationships, i.e. <Subject, Predicate, Object> triplets, is a challenging Scene Understanding task approached in the past via linguistic priors or spatial information in a single feature branch. We introduce a new deeply supervised two-branch architecture, the Multimodal Attentional Translation Embeddings, where the visual features of each branch are driven by a multimodal attentional mechanism that exploits spatio-linguistic similarities in a low-dimensional space. We present a variety of experiments comparing against all related approaches in the literature, as well as by re-implementing and fine-tuning several of them. Results on the commonly employed VRD dataset [1] show that the proposed method clearly outperforms all others, while we also justify our claims both quantitatively and qualitatively.
Cite
@article{arxiv.1902.05829,
title = {Deeply Supervised Multimodal Attentional Translation Embeddings for Visual Relationship Detection},
author = {Nikolaos Gkanatsios and Vassilis Pitsikalis and Petros Koutras and Athanasia Zlatintsi and Petros Maragos},
journal= {arXiv preprint arXiv:1902.05829},
year = {2019}
}