# Survey of Visual Question Answering: Datasets and Techniques

Source: [Survey of Visual Question Answering: Datasets and Techniques](<https://www.sota2.com/research/paper/68fa63531e209131ce9fe352>)

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

Data updated: 2025-10-25T20:39:15.914Z

Authors: Akshay Kumar Gupta

Published: 2017-05-10T17:30:17.000Z

## Abstract

Visual question answering (or VQA) is a new and exciting problem that combines natural language processing and computer vision techniques. We present a survey of the various datasets and models that have been used to tackle this task. The first part of the survey details the various datasets for VQA and compares them along some common factors. The second part of this survey details the different approaches for VQA, classified into four types: non-deep learning models, deep learning models without attention, deep learning models with attention, and other models which do not fit into the first three. Finally, we compare the performances of these approaches and provide some directions for future work.

## 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/1705.03865>)

[PDF](<https://arxiv.org/pdf/1705.03865.pdf>)