26 citations · 54 across the 5 of their papers we have counts for
5 papers
Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution
Guangpin Tao, Xiaozhong Ji, Wenzhuo Wang +6
Deep-learning based Super-Resolution (SR) methods have exhibited promising performance under non-blind setting where blur kernel is known. However, blur kernels of Low-Resolution (…
A Random CNN Sees Objects: One Inductive Bias of CNN and Its Applications
Yun-Hao Cao, Jianxin Wu
This paper starts by revealing a surprising finding: without any learning, a randomly initialized CNN can localize objects surprisingly well. That is, a CNN has an inductive bias t…
Rethinking Self-Supervised Learning: Small is Beautiful
Yun-Hao Cao, Jianxin Wu
Self-supervised learning (SSL), in particular contrastive learning, has made great progress in recent years. However, a common theme in these methods is that they inherit the learn…
DML-GANR: Deep Metric Learning With Generative Adversarial Network Regularization for High Spatial Resolution Remote Sensing Image Retrieval
Yun Cao, Yuebin Wang, Junhuan Peng +4
With a small number of labeled samples for training, it can save considerable manpower and material resources, especially when the amount of high spatial resolution remote sensing…
SLCRF: Subspace Learning with Conditional Random Field for Hyperspectral Image Classification
Yun Cao, Jie Mei, Yuebin Wang +5
Subspace learning (SL) plays an important role in hyperspectral image (HSI) classification, since it can provide an effective solution to reduce the redundant information in the im…