A survey on graph neural networks and graph transformers in computer vision: A task-oriented perspective
Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …
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 …
Superhypergraph neural networks and plithogenic graph neural networks: Theoretical foundations
T Fujita - arXiv preprint arXiv:2412.01176, 2024 - arxiv.org
Hypergraphs extend traditional graphs by allowing edges to connect multiple nodes, while
superhypergraphs further generalize this concept to represent even more complex …
superhypergraphs further generalize this concept to represent even more complex …
Attention, please! A survey of neural attention models in deep learning
A de Santana Correia, EL Colombini - Artificial Intelligence Review, 2022 - Springer
In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …
limited ability to process competing sources, attention mechanisms select, modulate, and …
Multimodal remote sensing image segmentation with intuition-inspired hypergraph modeling
Multimodal remote sensing (RS) image segmentation aims to comprehensively utilize
multiple RS modalities to assign pixel-level semantics to the studied scenes, which can …
multiple RS modalities to assign pixel-level semantics to the studied scenes, which can …
Multimodal federated learning: Concept, methods, applications and future directions
Multimodal learning mines and analyzes multimodal data in reality to better understand and
appreciate the world around people. However, how to exploit this rich multimodal data …
appreciate the world around people. However, how to exploit this rich multimodal data …
Hypergraph transformer for skeleton-based action recognition
Skeleton-based action recognition aims to recognize human actions given human joint
coordinates with skeletal interconnections. By defining a graph with joints as vertices and …
coordinates with skeletal interconnections. By defining a graph with joints as vertices and …
Continual image deraining with hypergraph convolutional networks
Image deraining is a challenging task since rain streaks have the characteristics of a
spatially long structure and have a complex diversity. Existing deep learning-based methods …
spatially long structure and have a complex diversity. Existing deep learning-based methods …
Coarse-to-fine reasoning for visual question answering
Bridging the semantic gap between image and question is an important step to improve the
accuracy of the Visual Question Answering (VQA) task. However, most of the existing VQA …
accuracy of the Visual Question Answering (VQA) task. However, most of the existing VQA …
Sigma++: Improved semantic-complete graph matching for domain adaptive object detection
Domain Adaptive Object Detection (DAOD) generalizes the object detector from an
annotated domain to a label-free novel one. Recent works estimate prototypes (class …
annotated domain to a label-free novel one. Recent works estimate prototypes (class …