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Deep Attention-guided Graph Clustering with Dual Self-supervision
Zhihao Peng, Hui Liu, Yuheng Jia +1
Existing deep embedding clustering works only consider the deepest layer to learn a feature embedding and thus fail to well utilize the available discriminative information from cl…
Adaptive Attribute and Structure Subspace Clustering Network
Zhihao Peng, Hui Liu, Yuheng Jia +1
Deep self-expressiveness-based subspace clustering methods have demonstrated effectiveness. However, existing works only consider the attribute information to conduct the self-expr…
Learning Dynamic Interpolation for Extremely Sparse Light Fields with Wide Baselines
Mantang Guo, Jing Jin, Hui Liu +1
In this paper, we tackle the problem of dense light field (LF) reconstruction from sparsely-sampled ones with wide baselines and propose a learnable model, namely dynamic interpola…
Semantic-embedded Unsupervised Spectral Reconstruction from Single RGB Images in the Wild
Zhiyu Zhu, Hui Liu, Junhui Hou +2
This paper investigates the problem of reconstructing hyperspectral (HS) images from single RGB images captured by commercial cameras, \textbf{without} using paired HS and RGB imag…
Deep Amended Gradient Descent for Efficient Spectral Reconstruction from Single RGB Images
Zhiyu Zhu, Hui Liu, Junhui Hou +2
This paper investigates the problem of recovering hyperspectral (HS) images from single RGB images. To tackle such a severely ill-posed problem, we propose a physically-interpretab…
Attention-driven Graph Clustering Network
Zhihao Peng, Hui Liu, Yuheng Jia +1
The combination of the traditional convolutional network (i.e., an auto-encoder) and the graph convolutional network has attracted much attention in clustering, in which the auto-e…