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 presents a comprehensive comparative study of five advanced VQA models: ABC-CNN, KICNLE, Masked Vision and Language Modeling, BLIP-2, and OFA, each employing distinct methodologies to address these challenges.

本文针对视觉问答（VQA）领域中存在的数据集偏见、模型复杂性受限、常识推理缺口等问题进行了研究。通过比较五种先进的VQA模型，提出了各自独特的方法，致力于有效应对这些挑战，旨在推动VQA模型的鲁棒性和实用性。

探索视觉问答的先进技术：全面比较