Szczegóły publikacji
Opis bibliograficzny
Graph neural networks in computer vision - architectures, datasets and common approaches / Maciej KRZYWDA, Szymon ŁUKASIK, Amir H. Gandomi // W: IJCNN 2022 [Dokument elektroniczny] : International Joint Conference on Neural Networks : Padua, Italy, 18–23 July 2022 : proceedings / IEEE. — Wersja do Windows. — Dane tekstowe. — Piscataway : IEEE, cop. 2022. — (Proceedings of ... International Joint Conference on Neural Networks ; ISSN 2161-4393). — Konferencja zorganizowana w ramach IEEE World Congress on Computational Intelligence (IEEE WCCI 2022). — e-ISBN: 978-1-7281-8671-9. — S. [1–10]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [7–10], Abstr. — Publikacja dostępna online od: 2022-09-30. — S. Łukasik - dod. afiliacja: Systems Research Institute, Polish Academy of Sciences, Warsaw
Autorzy (3)
- AGHKrzywda Maciej
- AGHŁukasik Szymon
- Gandomi Amir H.
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 142997 |
|---|---|
| Data dodania do BaDAP | 2022-10-29 |
| Tekst źródłowy | URL |
| DOI | 10.1109/IJCNN55064.2022.9892658 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | International Joint Conference on Neural Networks |
| Czasopismo/seria | Proceedings of ... International Joint Conference on Neural Networks |
Abstract
Graph Neural Networks (GNNs) are a family of graph networks inspired by mechanisms existing between nodes on a graph. In recent years there has been an increased interest in GNN and their derivatives, i.e., Graph Attention Networks (GAT), Graph Convolutional Networks (GCN), and Graph Recurrent Networks (GRN). An increase in their usability in computer vision is also observed. The number of GNN applications in this field continues to expand; it includes video analysis and understanding, action and behavior recognition, computational photography, image and video synthesis from zero or few shots, and many more. This contribution aims to collect papers published about GNN-based approaches towards computer vision. They are described and summarized from three perspectives. Firstly, we investigate the architectures of Graph Neural Networks and their derivatives used in this area to provide accurate and explainable recommendations for the ensuing investigations. As for the other aspect, we also present datasets used in these works. Finally, using graph analysis, we also examine relations between GNN-based studies in computer vision and potential sources of inspiration identified outside of this field.