# Answering Visual What-If Questions: From Actions to Predicted Scene Descriptions

Source: [Answering Visual What-If Questions: From Actions to Predicted Scene Descriptions](<https://www.sota2.com/research/paper/68fa65141e209131cea08119>)

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

Data updated: 2025-10-25T21:17:53.140Z

Authors: M. Wagner, H. Basevi, R. Shetty, W. Li, M. Malinowski, M. Fritz, A. Leonardis

Published: 2018-09-11T07:22:28.000Z

## Abstract

In-depth scene descriptions and question answering tasks have greatly increased the scope of today's definition of scene understanding. While such tasks are in principle open ended, current formulations primarily focus on describing only the current state of the scenes under consideration. In contrast, in this paper, we focus on the future states of the scenes which are also conditioned on actions. We posit this as a question answering task, where an answer has to be given about a future scene state, given observations of the current scene, and a question that includes a hypothetical action. Our solution is a hybrid model which integrates a physics engine into a question answering architecture in order to anticipate future scene states resulting from object-object interactions caused by an action. We demonstrate first results on this challenging new problem and compare to baselines, where we outperform fully data-driven end-to-end learning approaches.

## 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/1809.03707>)

[PDF](<https://arxiv.org/pdf/1809.03707.pdf>)