Back to Search
Start Over
Instance-Aware Deep Graph Learning for Multi-Label Classification
- Source :
- IEEE Transactions on Multimedia. 25:90-99
- Publication Year :
- 2023
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2023.
-
Abstract
- Graph convolutional neural network (GCN) has effectively boosted the multi-label image recognition task by modeling correlation among labels. In previous methods, label correlation is computed based on statistical information through label diffusion, and therefore the same for all samples. This, however, makes graph inference on labels insufficient to handle huge variations among numerous image instances. In this paper, we propose an instance-aware graph convolutional neural network (IA_GCN) framework for the multi-label classification. As a whole, two fused branches of sub-networks are involved in the framework: a global branch modeling the whole image and a local branch exploring dependencies among regions of interests (ROIs). For both the branches, an image-dependent label correlation matrix (ID_LCM), fusing both the statistical LCM and an individual one of each image instance, is constructed to inject adaptive information of label-awareness into the learned features of the model through graph convolution. Specifically, the individual LCM of each image is obtained by mining the label dependencies based on the predicted label scores of those detected ROIs. In this process, considering the contribution differences of ROIs to multi-label classification, variational inference is introduced to learn adaptive scaling factors for those ROIs by considering their complex distribution. Finally, extensive experiments on MS-COCO and VOC datasets show that our proposed approach outperforms existing state-of-the-art methods.
- Subjects :
- Multi-label classification
Covariance matrix
Computer science
business.industry
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Process (computing)
Inference
Pattern recognition
Convolutional neural network
Computer Science Applications
Convolution
Image (mathematics)
ComputingMethodologies_PATTERNRECOGNITION
Computer Science::Computer Vision and Pattern Recognition
Signal Processing
Media Technology
Graph (abstract data type)
Artificial intelligence
Electrical and Electronic Engineering
business
Subjects
Details
- ISSN :
- 19410077 and 15209210
- Volume :
- 25
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Multimedia
- Accession number :
- edsair.doi...........f46709407816ce259ff68569153b473f