most citedRS-MetaNet: Deep meta metric learning for few-shot remote sensing scene classification

98 citations · 207 across the 13 of their papers we have counts for

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cs.CV202215 cited

A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data

Qifan Jin, Li Chen

The surface defect detection method based on visual perception has been widely used in industrial quality inspection. Because defect data are not easy to obtain and the annotation…

cs.CV202098 cited

RS-MetaNet: Deep meta metric learning for few-shot remote sensing scene classification

Haifeng Li, Zhenqi Cui, Zhiqing Zhu +4

Training a modern deep neural network on massive labeled samples is the main paradigm in solving the scene classification problem for remote sensing, but learning from only a few d…

cs.CV20201 cited

Generative Model without Prior Distribution Matching

Cong Geng, Jia Wang, Li Chen +1

Variational Autoencoder (VAE) and its variations are classic generative models by learning a low-dimensional latent representation to satisfy some prior distribution (e.g., Gaussia…

cs.CV2020

Simulating Unknown Target Models for Query-Efficient Black-box Attacks

Chen Ma, Li Chen, Jun-Hai Yong

Many adversarial attacks have been proposed to investigate the security issues of deep neural networks. In the black-box setting, current model stealing attacks train a substitute…

cs.CV2020

Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement

Li Chen, Thomas Hatsukami, Jenq-Neng Hwang +1

Automatically labeling intracranial arteries (ICA) with their anatomical names is beneficial for feature extraction and detailed analysis of intracranial vascular structures. There…

cs.CV20201 cited

AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results

Seungjun Nah, Sanghyun Son, Radu Timofte +1

Videos contain various types and strengths of motions that may look unnaturally discontinuous in time when the recorded frame rate is low. This paper reviews the first AIM challeng…