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-Page Document Visual Question Answering using Self-Attention Scoring Mechanism
Lei Kang, Rubèn Tito, Ernest Valveny, Dimosthenis Karatzas
ViOCRVQA: Novel Benchmark Dataset and Vision Reader for Visual Question Answering by Understanding Vietnamese Text in Images
Huy Quang Pham, Thang Kien-Bao Nguyen, Quan Van Nguyen, Dan Quang Tran, Nghia Hieu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen
ViTextVQA: A Large-Scale Visual Question Answering Dataset for Evaluating Vietnamese Text Comprehension in Images
Quan Van Nguyen, Dan Quang Tran, Huy Quang Pham, Thang Kien-Bao Nguyen, Nghia Hieu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen
Find The Gap: Knowledge Base Reasoning For Visual Question Answering
Elham J. Barezi, Parisa Kordjamshidi
Self-Training Large Language Models for Improved Visual Program Synthesis With Visual Reinforcement
Zaid Khan, Vijay Kumar BG, Samuel Schulter, Yun Fu, Manmohan Chandraker
Joint Visual and Text Prompting for Improved Object-Centric Perception with Multimodal Large Language Models
Songtao Jiang, Yan Zhang, Chenyi Zhou, Yeying Jin, Yang Feng, Jian Wu, Zuozhu Liu