A Survey of Datasets and Algorithms for Visual Question Answering

Temel Kavramlar

This survey paper provides a comprehensive overview of existing datasets and algorithms in the field of Visual Question Answering (VQA), categorizing and analyzing their strengths, weaknesses, and specific focuses.

Özet

Bibliographic Information:

Kabir, R., Haque, N., Islam, M. S., & Jannat, M. (2016). A Comprehensive Survey on Visual Question Answering Datasets and Algorithms. IEEE Access, 4, 6997–7021.

Research Objective:

This survey paper aims to provide a structured and detailed overview of the rapidly developing field of Visual Question Answering (VQA), focusing on the analysis of existing datasets and algorithms.

Methodology:

The authors conduct a comprehensive review of published literature on VQA, categorizing datasets based on characteristics like image source (real, synthetic, diagnostic, knowledge-based) and algorithms based on their approaches to image representation, question representation, fusion/attention mechanisms, and answering methods.

Key Findings:

• The paper highlights the diversity in VQA datasets, each with strengths and limitations regarding scale, image complexity, question types, bias presence, and evaluation metrics.
• It categorizes VQA algorithms based on their core components, including image and question representation techniques, fusion and attention mechanisms for combining visual and textual information, and answering strategies for generating accurate responses.
• The survey identifies key challenges in VQA, such as handling diverse question types, addressing dataset biases, achieving compositional reasoning, incorporating external knowledge, and developing robust evaluation metrics.

Main Conclusions:

The authors conclude that while VQA has witnessed significant progress, addressing the identified challenges is crucial for developing truly robust and generalizable VQA systems. They emphasize the need for more diverse and balanced datasets, advanced reasoning and knowledge integration techniques, and standardized evaluation metrics for fair model comparison.

Significance:

This survey provides a valuable resource for researchers and practitioners in VQA by offering a structured overview of the field's current state, highlighting key advancements, and identifying areas for future research and development.

Limitations and Future Research:

As a survey paper, it primarily focuses on summarizing existing work and does not present novel research findings. The authors suggest future research directions, including exploring new dataset creation methods, developing more sophisticated algorithms for reasoning and knowledge integration, and establishing standardized evaluation protocols for VQA systems.

İstatistikler

In VQA-v1, 32.37% of the questions are “yes/no” binary questions.
In VQA-v1, improving accuracy on “Is/Are” questions by 15% will increase overall accuracy by over 5% but answering all “Why/Where” questions correctly will increase overall accuracy by only 4.1%.
27% of answers in the Visual Genome dataset contain three or more words.
59% of “why” questions in the VQA-v1 dataset have no answer with more than two annotators.

Alıntılar

Önemli Bilgiler Şuradan Elde Edildi

A Comprehensive Survey on Visual Question Answering Datasets and Algorithms

by Raihan Kabir... : arxiv.org 11-19-2024

https://arxiv.org/pdf/2411.11150.pdf