Fair-VQA: Fairness-Aware Visual Question Answering Through Sensitive Attribute Prediction
IEEE Access, 2020 Visual Question Answering (VQA) is a task that answers questions on given images. Although previous works achieve a great improvement in VQA performance, they do not consider the fairness of answers in terms of ethically sensitive attributes, such as ...
Sungho Park, Hyeran Byun, Sunhee Hwang
exaly +3 more sources
PMA-VQA: Progressive Multi-Scale Feature Fusion with Spatially Adaptive Attention for Remote Sensing Visual Question Answering [PDF]
SensorsRemote sensing visual question answering (RS-VQA) is essential to intelligent Earth observation, as it supports interactive querying of high-resolution aerial images.
Yifei He, Chen Qiu, Jinguang Gu
doaj +2 more sources
Enhancing accessibility: a multi-level platform for visual question answering in diabetic retinopathy for individuals with disabilities [PDF]
Frontiers in Artificial IntelligenceIndividuals with visual disabilities possess impairments that affect their ability to perceive visual information, ranging from partial to complete vision loss. Visual disabilities affect about 2.2 billion people globally.
Sarah Alotaibi +3 more
doaj +2 more sources
Talking to the Brain: Using Large Language Models as Proxies to Model Brain Semantic Features. [PDF]
Hum Brain MappWe introduce a novel paradigm using LLMs to map the brain's semantic space with greater ecological validity. This provides a robust framework for integrating naturalistic fMRI data and reveals a hierarchically organized semantic similarity space across the cerebral cortex.
Liu X, Zhang Z, Nie J.
europepmc +2 more sources
Overcoming Language Priors via Shuffling Language Bias for Robust Visual Question Answering
IEEE Access, 2023 Recent research has revealed the notorious language prior problem in visual question answering (VQA) tasks based on visual-textual interaction, which indicates that well-developed VQA models rely on learning shortcuts from questions without fully ...
J. Zhao, Z. Yu, X. Zhang, Y. Yang
doaj +1 more source
KonVid-150k: A Dataset for No-Reference Video Quality Assessment of Videos in-the-Wild
IEEE Access, 2021 Video quality assessment (VQA) methods focus on particular degradation types, usually artificially induced on a small set of reference videos. Hence, most traditional VQA methods under-perform in-the-wild.
Franz Gotz-Hahn +3 more
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COIN: Counterfactual Image Generation for Visual Question Answering Interpretation
Sensors, 2022 Due to the significant advancement of Natural Language Processing and Computer Vision-based models, Visual Question Answering (VQA) systems are becoming more intelligent and advanced.
Zeyd Boukhers +2 more
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Knowledge-based Visual Question Answering:A Survey [PDF]
Jisuanji kexue, 2023 As an important presentation form of the completeness of artificial intelligence and the visual Turing test,visual question answering(VQA),coupled with its potential application value,has received extensive attention from computer vision and na-tural ...
WANG Ruiping, WU Shihong, ZHANG Meihang, WANG Xiaoping
doaj +1 more source
A Comprehensive Review and Open Challenges on Visual Question Answering Models
AiBi Revista de Investigación, Administración e Ingeniería, 2023 Users are now able to actively interact with images and pose different questions based on images, thanks to recent developments in artificial intelligence. In turn, a response in a natural language answer is expected.
Fasi Ahamad Shaik +4 more
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Review of Research on Video Quality Assessment Based on Deep Learning
Jisuanji kexue yu tansuo, 2021 Video quality assessment (VQA) is based on the subjective quality assessment results of the human eye, using models to evaluate distorted videos. It is difficult for traditional assessment methods to make subjective assessment results consistent with ...
TAN Yaya, KONG Guangqian
doaj +1 more source