Search Results for author: Guangfeng Lin

Found 7 papers, 2 papers with code

Deep graph learning for semi-supervised classification

no code implementations29 May 2020 Guangfeng Lin, Xiaobing Kang, Kaiyang Liao, Fan Zhao, Yajun Chen

Existing methods mostly combine the computational layer and the related losses into GCN for exploring the global graph(measuring graph structure from all data samples) or local graph (measuring graph structure from local data samples).

General Classification Graph Learning

High-order structure preserving graph neural network for few-shot learning

1 code implementation29 May 2020 Guangfeng Lin, Ying Yang, Yindi Fan, Xiaobing Kang, Kaiyang Liao, Fan Zhao

Most existing methods try to model the similarity relationship of the samples in the intra tasks, and generalize the model to identify the new categories.

Few-Shot Learning

Structure fusion based on graph convolutional networks for semi-supervised classification

no code implementations2 Jul 2019 Guangfeng Lin, Jing Wang, Kaiyang Liao, Fan Zhao, Wanjun Chen

By solving this function, we can simultaneously obtain the fusion spectral embedding from the multi-view data and the fusion structure as adjacent matrix to input graph convolutional networks for semi-supervised classification.

General Classification Node Classification

Three-Stream Convolutional Neural Network With Multi-Task and Ensemble Learning for 3D Action Recognition

no code implementations The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019 2019 Duohan Liang, Guoliang Fan, Guangfeng Lin, Wanjun Chen, Xiaorong Pan, Hong Zhu

In this paper, we propose a three-stream convolutional neural network (3SCNN) for action recognition from skeleton sequences, which aims to thoroughly and fully exploit the skeleton data by extracting, learning, fusing and inferring multiple motion-related features, including 3D joint positions and joint displacements across adjacent frames as well as oriented bone segments.

Ensemble Learning Skeleton Based Action Recognition

Transfer feature generating networks with semantic classes structure for zero-shot learning

no code implementations6 Mar 2019 Guangfeng Lin, Wanjun Chen, Kaiyang Liao, Xiaobing Kang, Caixia Fan

To alleviate the negative influence of this inconsistence for ZSL and GZSL, transfer feature generating networks with semantic classes structure (TFGNSCS) is proposed to construct networks model for improving the performance of ZSL and GZSL.

General Classification Zero-Shot Learning

Class label autoencoder for zero-shot learning

no code implementations25 Jan 2018 Guangfeng Lin, Caixia Fan, Wanjun Chen, Yajun Chen, Fan Zhao

CLA can not only build a uniform framework for adapting to multi-semantic embedding spaces, but also construct the encoder-decoder mechanism for constraining the bidirectional projection between the feature space and the class label space.

Zero-Shot Learning

Structure propagation for zero-shot learning

1 code implementation27 Nov 2017 Guangfeng Lin, Yajun Chen, Fan Zhao

It is difficult to capture the relationship among image classes due to unseen classes, so that the manifold structure of image classes often is ignored in ZSL.

Zero-Shot Learning

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