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
RS-MoE: Mixture of Experts for Remote Sensing Image Captioning and Visual Question Answering
Hui Lin, Danfeng Hong, Shuhang Ge, Chuyao Luo, Kai Jiang, Hao Jin, Congcong Wen
Goal-Oriented Semantic Communication for Wireless Visual Question Answering with Scene Graphs
Sige Liu, Nan Li, Yansha Deng
A Visual Question Answering Method for SAR Ship: Breaking the Requirement for Multimodal Dataset Construction and Model Fine-Tuning
Fei Wang, Chengcheng Chen, Hongyu Chen, Yugang Chang, Weiming Zeng
NaturalBench: Evaluating Vision-Language Models on Natural Adversarial Samples
Baiqi Li, Zhiqiu Lin, Wenxuan Peng, Jean de Dieu Nyandwi, Daniel Jiang, Zixian Ma, Simran Khanuja, Ranjay Krishna, Graham Neubig, Deva Ramanan
ViConsFormer: Constituting Meaningful Phrases of Scene Texts using Transformer-based Method in Vietnamese Text-based Visual Question Answering
Nghia Hieu Nguyen, Tho Thanh Quan, Ngan Luu-Thuy Nguyen