# Predicting Motivations of Actions by Leveraging Text

arXiv: https://arxiv.org/abs/1406.5472 · HTML: https://mlsanity.com/paper/1406.5472/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2014.

## Built on by (most cited first, 6 of 6)

- [The Abduction of Sherlock Holmes: A Dataset for Visual Abductive Reasoning](https://mlsanity.com/paper/2202.04800/) (2022)
- [What Makes a Maze Look Like a Maze?](https://mlsanity.com/paper/2409.08202/) (2024)
- [Deep Learning Approaches for Multimodal Intent Recognition: A Survey](https://mlsanity.com/paper/2507.22934/) (2025)
- [CoSIm: Commonsense Reasoning for Counterfactual Scene Imagination](https://mlsanity.com/paper/2207.03961/) (2022)
- [Hybrid Reasoning Network for Video-based Commonsense Captioning](https://mlsanity.com/paper/2108.02365/) (2021)
- [Understanding in Artificial Intelligence](https://mlsanity.com/paper/2101.06573/) (2021)

## Abstract (arXiv)

Understanding human actions is a key problem in computer vision. However, recognizing actions is only the first step of understanding what a person is doing. In this paper, we introduce the problem of predicting why a person has performed an action in images. This problem has many applications in human activity understanding, such as anticipating or explaining an action. To study this problem, we introduce a new dataset of people performing actions annotated with likely motivations. However, the information in an image alone may not be sufficient to automatically solve this task. Since humans can rely on their lifetime of experiences to infer motivation, we propose to give computer vision systems access to some of these experiences by using recently developed natural language models to mine knowledge stored in massive amounts of text. While we are still far away from fully understanding motivation, our results suggest that transferring knowledge from language into vision can help machines understand why people in images might be performing an action.