Learning Graph Convolutional Networks for Multi-Label Recognition and Applications
| Autor: | Yanwen Guo, Peng Wang, Xiu-Shen Wei, Zhao-Min Chen |
|---|---|
| Rok vydání: | 2023 |
| Předmět: |
Word embedding
business.industry Computer science Applied Mathematics Pattern recognition Directed graph Facial recognition system Convolutional neural network Graph Set (abstract data type) Computational Theory and Mathematics Artificial Intelligence Graph (abstract data type) Computer Vision and Pattern Recognition Artificial intelligence Representation (mathematics) business Classifier (UML) Software |
| Zdroj: | IEEE Transactions on Pattern Analysis and Machine Intelligence. 45:6969-6983 |
| ISSN: | 1939-3539 0162-8828 |
| Popis: | The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model label dependencies to improve recognition performance. To capture and explore such important information, we propose Graph Convolutional Networks based models for multi-label recognition, where directed graphs are constructed over classes and information is propagated between classes to learn inter-dependent class-level representations. Following this idea, we design two particular models that approach multi-label classification from different views. In our first model, the prior knowledge about the class dependencies is integrated into classifier learning. Specifically, we propose Classifier-Learning-GCN to map class-level semantic representations (\eg, word embedding) into classifiers that maintain the inter-class topology. In our second model, we decompose the visual representation of an image into a set of label-aware features and propose Prediction-Learning-GCN to encode such features into inter-dependent image-level prediction scores. Furthermore, we also present an effective correlation matrix construction approach to capture inter-class relationships and consequently guide information propagation among classes. Empirical results on generic multi-label recognition demonstrate that the effectiveness of both two proposed models. Moreover, the proposed methods also show advantages in other multi-label related applications. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|