Exploring Advanced Techniques for Visual Question Answering: A Comprehensive Comparison

TL;DR

This paper provides a comprehensive comparison of advanced VQA models, analyzing dataset challenges, model approaches, and performance to guide future research in multimodal reasoning.

Contribution

It offers a detailed comparative analysis of five state-of-the-art VQA models and discusses ongoing challenges in dataset bias and model generalization.

Findings

Identified strengths and weaknesses of each model

Highlighted dataset biases affecting model performance

Provided insights into future directions for VQA research

Abstract

Visual Question Answering (VQA) has emerged as a pivotal task in the intersection of computer vision and natural language processing, requiring models to understand and reason about visual content in response to natural language questions. Analyzing VQA datasets is essential for developing robust models that can handle the complexities of multimodal reasoning. Several approaches have been developed to examine these datasets, each offering distinct perspectives on question diversity, answer distribution, and visual-textual correlations. Despite significant progress, existing VQA models face challenges related to dataset bias, limited model complexity, commonsense reasoning gaps, rigid evaluation methods, and generalization to real world scenarios. This paper offers a detailed study of the original VQA dataset, baseline models and methods along with a comparative study of five advanced VQA models, ABC-CNN, KICNLE, Masked Vision and Language Modeling, BLIP-2, and OFA, each employing distinct methods to address these ongoing challenges.