4 papers
MetaRA: Metamorphic Robustness Assessment for Multimodal Large Language Model-based Visual Question Answering Systems
Quanxing Xu, Yuhao Tian, Ling Zhou +4
Visual Question Answering (VQA), as the representative multimodal task, serves as a key benchmark for evaluating the reasoning capabilities of Multimodal Large Language Models (MLL…
Enhancing Visual Question Answering with Multimodal LLMs via Chain-of-Question Guided Retrieval-Augmented Generation
Quanxing Xu, Ling Zhou, Xian Zhong +3
With advances in multimodal research and deep learning, Multimodal Large Language Models (MLLMs) have emerged as a powerful paradigm for a wide range of multimodal tasks. As a core…
OAD-Promoter: Enhancing Zero-shot VQA using Large Language Models with Object Attribute Description
Quanxing Xu, Ling Zhou, Feifei Zhang +2
Large Language Models (LLMs) have become a crucial tool in Visual Question Answering (VQA) for handling knowledge-intensive questions in few-shot or zero-shot scenarios. However, t…
QIRL: Optimized Question-Image Relation Learning for Bias-Robust Visual Question Answering
Quanxing Xu, Ling Zhou, Xian Zhong +3
Existing bias mitigation methods for Visual Question Answering (VQA), a typical Artificial intelligence application, endure two main limitations. First, they fail to capture the op…