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ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning
- Source :
- CVPR
- Publication Year :
- 2021
-
Abstract
- Recently, the transductive graph-based methods have achieved great success in the few-shot classification task. However, most existing methods ignore exploring the class-level knowledge that can be easily learned by humans from just a handful of samples. In this paper, we propose an Explicit Class Knowledge Propagation Network (ECKPN), which is composed of the comparison, squeeze and calibration modules, to address this problem. Specifically, we first employ the comparison module to explore the pairwise sample relations to learn rich sample representations in the instance-level graph. Then, we squeeze the instance-level graph to generate the class-level graph, which can help obtain the class-level visual knowledge and facilitate modeling the relations of different classes. Next, the calibration module is adopted to characterize the relations of the classes explicitly to obtain the more discriminative class-level knowledge representations. Finally, we combine the class-level knowledge with the instance-level sample representations to guide the inference of the query samples. We conduct extensive experiments on four few-shot classification benchmarks, and the experimental results show that the proposed ECKPN significantly outperforms the state-of-the-art methods.<br />Accepted by CVPR2021
- Subjects :
- FOS: Computer and information sciences
Class (computer programming)
Knowledge representation and reasoning
Computer science
Calibration (statistics)
business.industry
Computer Science - Artificial Intelligence
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Inference
Sample (statistics)
Machine learning
computer.software_genre
Artificial Intelligence (cs.AI)
Discriminative model
Graph (abstract data type)
Pairwise comparison
Artificial intelligence
business
computer
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- CVPR
- Accession number :
- edsair.doi.dedup.....dc0b6249deeced0f53bde497292b7719