对比五种先进模型，提升视觉问答的推理能力与泛化性能。
Exploring Advanced Techniques for Visual Question Answering: A Comprehensive Comparison
- 采用多种方法解决视觉与语言融合中的复杂推理问题。
- 在原始VQA数据集上验证模型表现，突出跨模态理解优势。
- 适合关注多模态模型设计与评估的科研人员参考。
视觉问答（VQA）作为计算机视觉与自然语言处理交叉领域的重要任务，要求模型理解并回答关于图像的自然语言问题。分析VQA数据集对于开发能够应对多模态推理复杂性的鲁棒模型至关重要。已有多种方法用于研究数据集特性，涵盖问题多样性、答案分布及视觉-文本关联。尽管取得显著进展，现有模型仍面临数据集偏差、模型复杂度有限、常识推理缺失、评估方式僵化以及现实场景泛化能力不足等挑战。本文对原始VQA数据集、基线模型及方法进行了详细研究，并系统比较了五种先进VQA模型：ABC-CNN、KICNLE、Masked Vision and Language Modeling、BLIP-2和OFA。这些模型采用不同策略应对上述挑战，展现出多样化的技术路径。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。