# Visual-Semantic Graph Attention Networks for Human-Object Interaction Detection

arXiv: https://arxiv.org/abs/2001.02302 · HTML: https://mlsanity.com/paper/2001.02302/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2020.

## Built on by (most cited first, 1 of 1)

- [Connecting the Dots: Graph Neural Network Powered Ensemble and Classification of Medical Images](https://mlsanity.com/paper/2311.07321/) (2023)

## Abstract (arXiv)

In scene understanding, robotics benefit from not only detecting individual scene instances but also from learning their possible interactions. Human-Object Interaction (HOI) Detection infers the action predicate on a <human, predicate, object> triplet. Contextual information has been found critical in inferring interactions. However, most works only use local features from single human-object pair for inference. Few works have studied the disambiguating contribution of subsidiary relations made available via graph networks. Similarly, few have learned to effectively leverage visual cues along with the intrinsic semantic regularities contained in HOIs. We contribute a dual-graph attention network that effectively aggregates contextual visual, spatial, and semantic information dynamically from primary human-object relations as well as subsidiary relations through attention mechanisms for strong disambiguating power. We achieve comparable results on two benchmarks: V-COCO and HICO-DET. Code is available at urlhttps://github.com/birlrobotics/vs-gats.