# Understanding Spatial Relations through Multiple Modalities

arXiv: https://arxiv.org/abs/2007.09551 · HTML: https://mlsanity.com/paper/2007.09551/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2020.

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

- [Do language models have coherent mental models of everyday things?](https://mlsanity.com/paper/2212.10029/) (2022)
- [More than Correlation: Do Large Language Models Learn Causal Representations of Space?](https://mlsanity.com/paper/2312.16257/) (2023)
- [Visual Spatial Description: Controlled Spatial-Oriented Image-to-Text Generation](https://mlsanity.com/paper/2210.11109/) (2022)
- [A Hybrid Model of Classification and Generation for Spatial Relation Extraction](https://mlsanity.com/paper/2208.06961/) (2022)

## Abstract (arXiv)

Recognizing spatial relations and reasoning about them is essential in multiple applications including navigation, direction giving and human-computer interaction in general. Spatial relations between objects can either be explicit -- expressed as spatial prepositions, or implicit -- expressed by spatial verbs such as moving, walking, shifting, etc. Both these, but implicit relations in particular, require significant common sense understanding. In this paper, we introduce the task of inferring implicit and explicit spatial relations between two entities in an image. We design a model that uses both textual and visual information to predict the spatial relations, making use of both positional and size information of objects and image embeddings. We contrast our spatial model with powerful language models and show how our modeling complements the power of these, improving prediction accuracy and coverage and facilitates dealing with unseen subjects, objects and relations.