Self-guided information for few-shot classification
作者:
Highlights:
• We propose the Self-Guided Information Convolution (SGI-Conv), which can accurately extract more distinctive features by regenerating high-level features mixture with low-level features.
• We design a hierarchical graph convolution network, graph convolution block network (GCBNet), sharing the adjacent matrix to deepen the depth of the graph convolutional network, which enhancing the aggregation ability.
• We conduct various experiments on the few-shot classification to demonstrate that our method achieves state-of-the-art performance on multiple benchmark datasets compared with other methods.
摘要
•We propose the Self-Guided Information Convolution (SGI-Conv), which can accurately extract more distinctive features by regenerating high-level features mixture with low-level features.•We design a hierarchical graph convolution network, graph convolution block network (GCBNet), sharing the adjacent matrix to deepen the depth of the graph convolutional network, which enhancing the aggregation ability.•We conduct various experiments on the few-shot classification to demonstrate that our method achieves state-of-the-art performance on multiple benchmark datasets compared with other methods.
论文关键词:Few-shot classification,Graph convolution network,Self-guided information
论文评审过程:Received 25 August 2021, Revised 11 March 2022, Accepted 26 June 2022, Available online 30 June 2022, Version of Record 7 July 2022.
论文官网地址:https://doi.org/10.1016/j.patcog.2022.108880