# Visual Question Answering using Deep Learning: A Survey and Performance Analysis

Source: [Visual Question Answering using Deep Learning: A Survey and Performance Analysis](<https://www.sota2.com/research/paper/68fa68fb1e209131cea1f81e>)

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

Data updated: 2025-10-28T13:56:45.965Z

Authors: Yash Srivastava, Vaishnav Murali, Shiv Ram Dubey, Snehasis Mukherjee

Published: 2019-08-27T07:03:03.000Z

## Abstract

The Visual Question Answering (VQA) task combines challenges for processing data with both Visual and Linguistic processing, to answer basic \`common sense' questions about given images. Given an image and a question in natural language, the VQA system tries to find the correct answer to it using visual elements of the image and inference gathered from textual questions. In this survey, we cover and discuss the recent datasets released in the VQA domain dealing with various types of question-formats and robustness of the machine-learning models. Next, we discuss about new deep learning models that have shown promising results over the VQA datasets. At the end, we present and discuss some of the results computed by us over the vanilla VQA model, Stacked Attention Network and the VQA Challenge 2017 winner model. We also provide the detailed analysis along with the challenges and future research directions.

## 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/1909.01860>)

[PDF](<https://arxiv.org/pdf/1909.01860.pdf>)