Visual Question Answering
Visual Question Answering (VQA) aims to enable computers to answer questions about images, requiring sophisticated integration of visual and linguistic understanding. Current research emphasizes improving model robustness and reliability, focusing on addressing issues like inconsistencies in responses, hallucinations, and the handling of unanswerable questions, often using large multimodal language models (MLLMs) like BLIP-2 and LLaVA. This field is crucial for advancing AI's ability to interact with the world in a more human-like way, with applications ranging from assistive technologies for visually impaired individuals to medical image analysis and automated data visualization evaluation.
Papers
Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering
Bowen Jiang, Zhijun Zhuang, Shreyas S. Shivakumar, Dan Roth, Camillo J. Taylor
MyVLM: Personalizing VLMs for User-Specific Queries
Yuval Alaluf, Elad Richardson, Sergey Tulyakov, Kfir Aberman, Daniel Cohen-Or
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models
Ziyi Yin, Muchao Ye, Tianrong Zhang, Jiaqi Wang, Han Liu, Jinghui Chen, Ting Wang, Fenglong Ma
II-MMR: Identifying and Improving Multi-modal Multi-hop Reasoning in Visual Question Answering
Jihyung Kil, Farideh Tavazoee, Dongyeop Kang, Joo-Kyung Kim
Multi-modal preference alignment remedies regression of visual instruction tuning on language model
Shengzhi Li, Rongyu Lin, Shichao Pei