# Learning to Act Properly: Predicting and Explaining Affordances from Images

Source: [Learning to Act Properly: Predicting and Explaining Affordances from Images](<https://www.sota2.com/research/paper/68fa64821e209131cea0504a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:06:03.000Z

Authors: Ching-Yao Chuang, Jiaman Li, Antonio Torralba, Sanja Fidler

Published: 2017-12-20T16:54:09.000Z

## Abstract

We address the problem of affordance reasoning in diverse scenes that appear in the real world. Affordances relate the agent's actions to their effects when taken on the surrounding objects. In our work, we take the egocentric view of the scene, and aim to reason about action-object affordances that respect both the physical world as well as the social norms imposed by the society. We also aim to teach artificial agents why some actions should not be taken in certain situations, and what would likely happen if these actions would be taken. We collect a new dataset that builds upon ADE20k, referred to as ADE-Affordance, which contains annotations enabling such rich visual reasoning. We propose a model that exploits Graph Neural Networks to propagate contextual information from the scene in order to perform detailed affordance reasoning about each object. Our model is showcased through various ablation studies, pointing to successes and challenges in this complex task.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1712.07576>)

[PDF](<https://arxiv.org/pdf/1712.07576.pdf>)