Results 51 to 60 of about 4,486 (211)

K-VQA: A visual question answering method

Journal of Hebei University of Science and Technology, 2020
The types of questions answered by the visual question answering of images and texts are roughly divided into two types. The first type is the questions that can get the answers directly from the images, and the second type is the questions that need the
Hongbin GAO, Jinying MAO, Huiyong WANG
doaj   +1 more source

A Review of Artificial Intelligence in Ophthalmology: Key Aspects, Challenges, and Future Directions

Eye &ENT Research, Volume 3, Issue 3, Page 125-154, September 2026.
ABSTRACT Artificial intelligence (AI) is increasingly reshaping ophthalmology because the specialty depends heavily on structured imaging, quantitative measurements, and repeatable diagnostic workflows. This review provides a clinically grounded and translationally oriented synthesis of AI in ophthalmology, covering methodological foundations ...
Partha Pratim Ray
wiley   +1 more source

Iterated learning for emergent systematicity in VQA

CoRR, 2021
Published as a conference paper at ICLR 2021.
Ankit Vani, Max Schwarzer, Yuchen Lu, Eeshan Dhekane, Aaron C. Courville   +4 more
openaire   +3 more sources

MedFuseT: A Transformer-Based Model for Advancing Medical Visual Question Answering

IEEE Access
As medicine and healthcare continue to evolve, Visual Question Answering (VQA) has emerged as an important application of artificial intelligence.
Hamza Mbarek, Omar Elharrouss, Hela Mahersia, Nabil Litayem   +3 more
doaj   +1 more source

AI Real‐Time Compositing Algorithm for Film and Television Special Effects Based on Latent Space Diffusion Model

Engineering Reports, Volume 8, Issue 9, September 2026.
The proposed latent diffusion compositing framework achieves high‐quality, temporally coherent, and efficient real‐time video compositing for practical AI‐driven visual effects applications. ABSTRACT Artificial intelligence–driven visual effects have become increasingly important in film and television production; however, achieving high‐quality real ...
Bowen An, Yue Xie, Yalun Lei
wiley   +1 more source

SURE-VQA: Systematic Understanding of Robustness Evaluation in Medical VQA Tasks

CoRR
TMLR 07 ...
Kim-Celine Kahl, Selen Erkan, Jeremias Traub, Carsten T. Lüth, Klaus H. Maier-Hein, Lena Maier-Hein, Paul F. Jaeger   +6 more
openaire   +4 more sources

Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training

Findings of the Association for Computational Linguistics: EMNLP 2022, 2022
Visual question answering (VQA) is a hallmark of vision and language reasoning and a challenging task under the zero-shot setting. We propose Plug-and-Play VQA (PNP-VQA), a modular framework for zero-shot VQA. In contrast to most existing works, which require substantial adaptation of pretrained language models (PLMs) for the vision modality, PNP-VQA ...
Anthony Meng Huat Tiong, Junnan Li 0001, Boyang Li 0001, Silvio Savarese, Steven C. H. Hoi   +4 more
openaire   +4 more sources

Prompt-Driven Fuzzing Debiasing Framework for Robust Visual Question Answering

Multimodal Technologies and Interaction
Visual Question Answering (VQA) systems have achieved impressive performance with the rise of large-scale vision–language models (VLMs). However, these models remain vulnerable to multiple forms of multimodal bias, severely limiting their robustness and ...
Yali Fan, Gangyu Huang, Qiwen Lu, Shengbo Chen   +3 more
doaj   +1 more source

MedFuseNet: An attention-based multimodal deep learning model for visual question answering in the medical domain

Scientific Reports, 2021
Medical images are difficult to comprehend for a person without expertise. The scarcity of medical practitioners across the globe often face the issue of physical and mental fatigue due to the high number of cases, inducing human errors during the ...
Dhruv Sharma, Sanjay Purushotham, Chandan K. Reddy   +2 more
doaj   +1 more source

Large Language Model Benchmarks in Medical Tasks

Medicine Advances, Volume 4, Issue 3, Page 316-341, September 2026.
This survey provides a comprehensive review of benchmark datasets for evaluating large language models in medical tasks, spanning text, image, and multimodal modalities, and covering key resources including MIMIC‐III/IV, BioASQ, PubMedQA, and CheXpert.
Lawrence K. Q. Yan, Qian Niu, Ming Li, Yichao Zhang, Caitlyn Heqi Yin, Cheng Fei, Benji Peng, Ziqian Bi, Pohsun Feng, Keyu Chen, Tianyang Wang, Yunze Wang, Silin Chen, Ming Liu, Junyu Liu, Xinyuan Song, Riyang Bao, Zekun Jiang, Ziyuan Qin   +18 more
wiley   +1 more source