Capsule Graph Neural Network for Multi-Label Image Recognition (Student Abstract)
Keywords:Capsule Networks, GCN, Image Recognition, Label Dependency
AbstractThis paper studies the problem of learning complex relationships between multi-labels for image recognition. Its challenges come from the rich and diverse semantic information in images. However, current methods cannot fully explore the mutual interactions among labels and do not explicitly model the label co-occurrence. To overcome these shortcomings, we innovatively propose CGML that consists of two crucial modules: 1) an image representation learning module that aims to complete the feature extraction of an image whose features are expressed in the form of primary capsules; 2) a label adaptive graph convolutional network module that leverages the popular graph convolutional networks with an adaptive label correlation graph to model label dependencies. Experiments show that our approach obviously outperforms the existing state-of-the-art methods.
How to Cite
Zheng, X., Liang, X., & Wu, B. (2022). Capsule Graph Neural Network for Multi-Label Image Recognition (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13117-13118. https://doi.org/10.1609/aaai.v36i11.21695
AAAI Student Abstract and Poster Program