# Explicit Knowledge-based Reasoning for Visual Question Answering

Source: [Explicit Knowledge-based Reasoning for Visual Question Answering](<https://www.sota2.com/research/paper/68fa62291e209131ce9f7bcb>)

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

Data updated: 2025-10-28T13:56:31.051Z

Authors: Peng Wang, Qi Wu, Chunhua Shen, Anton van den Hengel, Anthony Dick

Published: 2015-11-09T05:25:57.000Z

## Abstract

We describe a method for visual question answering which is capable of reasoning about contents of an image on the basis of information extracted from a large-scale knowledge base. The method not only answers natural language questions using concepts not contained in the image, but can provide an explanation of the reasoning by which it developed its answer. The method is capable of answering far more complex questions than the predominant long short-term memory-based approach, and outperforms it significantly in the testing. We also provide a dataset and a protocol by which to evaluate such methods, thus addressing one of the key issues in general visual ques- tion answering.

## 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/1511.02570>)

[PDF](<https://arxiv.org/pdf/1511.02570.pdf>)