Article: Based on transfer learning and graph neural network for animated clothing element recognition Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2025 Vol.17 No.7 pp.33 - 42 Abstract: Animation costume element recognition has become a major computer vision research area given the fast growth of the animation sector. Conventions for costume recognition include issues including significant stylistic variations and little annotating data. This work suggests a framework for animation costume element detection based on transfer learning (TL) and graph neural network (GNN) in order to solve these difficulties AnimCloth-TL-GNN. First, the framework models the spatial relationship between costume elements by the TL module; then, the GNN module models the knowledge from the source domain to the target domain; finally, the fusion module combines the features of both to improve the recognition effect of the model. Strong advantages in the animated costume element recognition job are shown by the experimental findings of the AnimCloth-TL-GNN framework, thereby enhancing the accuracy and has great generalising capacity and robustness. Inderscience Publishers - linking academia, business and industry through research

Title: Based on transfer learning and graph neural network for animated clothing element recognition

Authors: Yue Wu; Yunfeng Hu; Hao Luo

Addresses: School of Arts, Sanjiang University, Nanjing 210000, China ' School of Arts, Sanjiang University, Nanjing 210000, China; Faculty of Fine and Applied Arts and Cultural Science, Mahasarakham University, Maha Sarakham 44000, Thailand ' School of Arts, Sanjiang University, Nanjing 210000, China

Abstract: Animation costume element recognition has become a major computer vision research area given the fast growth of the animation sector. Conventions for costume recognition include issues including significant stylistic variations and little annotating data. This work suggests a framework for animation costume element detection based on transfer learning (TL) and graph neural network (GNN) in order to solve these difficulties AnimCloth-TL-GNN. First, the framework models the spatial relationship between costume elements by the TL module; then, the GNN module models the knowledge from the source domain to the target domain; finally, the fusion module combines the features of both to improve the recognition effect of the model. Strong advantages in the animated costume element recognition job are shown by the experimental findings of the AnimCloth-TL-GNN framework, thereby enhancing the accuracy and has great generalising capacity and robustness.

Keywords: transfer learning; TL; graph neural network; GNN; animated costume elements.

DOI: 10.1504/IJRIS.2025.146676

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.7, pp.33 - 42

Received: 31 Mar 2025
Accepted: 25 Apr 2025
Published online: 11 Jun 2025 *

Title: Based on transfer learning and graph neural network for animated clothing element recognition

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