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ASafe: Counterfactual Evidence-Aligned Adaptive Agent Collaboration for Safe and Effective Visual Question Answering
Quanxing Xu, Ling Zhou, Xian Zhong +4
Visual Question Answering (VQA) with Multimodal Large Language Models (MLLMs) requires not only producing safe and effective responses, but also grounding safety decisions in the m…
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…
Incorporating Scene Context and Semantic Labels for Enhanced Group-level Emotion Recognition
Qing Zhu, Wangdong Guo, Qirong Mao +3
Group-level emotion recognition (GER) aims to identify holistic emotions within a scene involving multiple individuals. Current existed methods underestimate the importance of visu…
A Survey of Deep Learning for Group-level Emotion Recognition
Xiaohua Huang, Jinke Xu, Wenming Zheng +2
With the advancement of artificial intelligence (AI) technology, group-level emotion recognition (GER) has emerged as an important area in analyzing human behavior. Early GER metho…