The multi-modal fusion in visual question answering: a review of attention mechanisms
Abstract Visual Question Answering (VQA) is a significant cross-disciplinary issue in the
fields of computer vision and natural language processing that requires a computer to output …
fields of computer vision and natural language processing that requires a computer to output …
An analysis of graph convolutional networks and recent datasets for visual question answering
AA Yusuf, F Chong, M Xianling - Artificial Intelligence Review, 2022 - Springer
Graph neural network is a deep learning approach widely applied on structural and non-
structural scenarios due to its substantial performance and interpretability recently. In a non …
structural scenarios due to its substantial performance and interpretability recently. In a non …
A survey of methods, datasets and evaluation metrics for visual question answering
Abstract Visual Question Answering (VQA) is a multi-disciplinary research problem that has
captured the attention of both computer vision as well as natural language processing …
captured the attention of both computer vision as well as natural language processing …
An improved attention and hybrid optimization technique for visual question answering
Abstract In Visual Question Answering (VQA), an attention mechanism has a critical role in
specifying the different objects present in an image or tells the machine where to focus by …
specifying the different objects present in an image or tells the machine where to focus by …
Image captioning improved visual question answering
Abstract Both Visual Question Answering (VQA) and image captioning are the problems
which involve Computer Vision (CV) and Natural Language Processing (NLP) domains. In …
which involve Computer Vision (CV) and Natural Language Processing (NLP) domains. In …
Graph neural networks for visual question answering: a systematic review
Recently, visual question answering (VQA) has gained considerable interest within the
computer vision and natural language processing (NLP) research areas. The VQA task …
computer vision and natural language processing (NLP) research areas. The VQA task …
Vqa and visual reasoning: An overview of recent datasets, methods and challenges
Artificial Intelligence (AI) and its applications have sparked extraordinary interest in recent
years. This achievement can be ascribed in part to advances in AI subfields including …
years. This achievement can be ascribed in part to advances in AI subfields including …
A COVID-19 X-ray image classification model based on an enhanced convolutional neural network and hill climbing algorithms
The classification of medical images is significant among researchers and physicians for the
early identification and clinical treatment of many disorders. Though, traditional classifiers …
early identification and clinical treatment of many disorders. Though, traditional classifiers …
Research on visual question answering based on GAT relational reasoning
Y Miao, W Cheng, S He, H Jiang - Neural Processing Letters, 2022 - Springer
Due to the diversity of questions in VQA, it brings new challenges to the construction of VQA
model. Existing VQA models focus on constructing a new attention mechanism, which …
model. Existing VQA models focus on constructing a new attention mechanism, which …
Evaluation of graph convolutional networks performance for visual question answering on reasoning datasets
AA Yusuf, F Chong, M Xianling - Multimedia Tools and Applications, 2022 - Springer
In the recent era, graph neural networks are widely used on vision-to-language tasks and
achieved promising results. In particular, graph convolution network (GCN) is capable of …
achieved promising results. In particular, graph convolution network (GCN) is capable of …