Visual Question Answering: A Survey on Techniques and Common Trends in Recent Literature

Visual Question Answering (VQA) is an emerging area of interest for researches, being a recent problem in natural language processing and image prediction. In this area, an algorithm needs to answer questions about certain images. As of the writing of this survey, 25 recent studies were analyzed. Besides, 6 datasets were analyzed and provided their link to download. In this work, several recent pieces of research in this area were investigated and a deeper analysis and comparison among them were provided, including results, the state-of-the-art, common errors, and possible points of improvement for future researchers.

READ FULL TEXT
research
08/27/2019

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

The Visual Question Answering (VQA) task combines challenges for process...
research
05/19/2023

Survey on software ISP methods based on Deep Learning

The entire Image Signal Processor (ISP) of a camera relies on several pr...
research
09/24/2017

Survey of Recent Advances in Visual Question Answering

Visual Question Answering (VQA) presents a unique challenge as it requir...
research
11/16/2021

Language bias in Visual Question Answering: A Survey and Taxonomy

Visual question answering (VQA) is a challenging task, which has attract...
research
04/06/2019

The Steep Road to Happily Ever After: An Analysis of Current Visual Storytelling Models

Visual storytelling is an intriguing and complex task that only recently...
research
07/21/2023

Robust Visual Question Answering: Datasets, Methods, and Future Challenges

Visual question answering requires a system to provide an accurate natur...
research
11/15/2022

A Survey for Efficient Open Domain Question Answering

Open domain question answering (ODQA) is a longstanding task aimed at an...

Please sign up or login with your details

Forgot password? Click here to reset