Part-GCNet: Partitioning Graph Convolutional Network for Multi-Label Recognition
Authors: 
Yuan Zhang, Tao Han, Bing Wei, Kuangrong HaoAuthors Info & Claims
Yuan Zhang, Tao Han, Bing Wei, Kuangrong HaoAuthors Info & ClaimsArticle No.: 5, Pages 1 - 5
Abstract
During the rapid development of deep learning, the multi-label recognition task has achieved pretty performance. Recently, the emergence of graph convolution network (GCN) has further improved the accuracy of multi-label recognition. However, in the learning process, how to better represent the feature information of labels and innovatively design structures to obtain good recognition performance is still unclear. To solve these problems, we propose a partitioning graph convolutional network framework for multi-label recognition. First, we segregate the computational graph into multiple sub-graphs. Then, we perform batch normalization operation on each output layer, which can further improve the recognition performance of the network. Finally, extensive experiments are carried out on a multi-label PPT dataset, showing that our proposed solution can greatly improve the feature information utilization of labels and improve the recognition performance.
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Index Terms
- Part-GCNet: Partitioning Graph Convolutional Network for Multi-Label Recognition
Index terms have been assigned to the content through auto-classification.
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Published In
December 2022
770 pages
ISBN:9781450398336
DOI:10.1145/3579654
Copyright © 2022 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 14 March 2023
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- Research-article
- Research
- Refereed limited
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- Fundamental Research Funds for the Central Universities
- Natural Science Foundation of Shanghai
- Shanghai Sailing Program
- National Natural Science Foundation of China
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ACAI 2022
ACAI 2022: 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence
December 23 - 25, 2022
Sanya, China
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Overall Acceptance Rate 173 of 395 submissions, 44%
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