# Deeply Supervised Multimodal Attentional Translation Embeddings for Visual Relationship Detection

arXiv: https://arxiv.org/abs/1902.05829 · HTML: https://mlsanity.com/paper/1902.05829/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Survey of Scene Graphs: Generation and Application](https://mlsanity.com/paper/2104.01111/) (2021)
- [Scene Graph Generation: A Comprehensive Survey](https://mlsanity.com/paper/2201.00443/) (2022)
- [Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection](https://mlsanity.com/paper/2204.04836/) (2022)
- [AGS: An Dataset and Taxonomy for Domestic Scene Sound Event Recognition](https://mlsanity.com/paper/2308.15726/) (2023)

## Abstract (arXiv)

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.