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20192022
most citedKernel-Induced Label Propagation by Mapping for Semi-Supervised Classification

34 citations · 72 across the 13 of their papers we have counts for

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15 papers · 1 filter

cs.CV20203 cited

Unsupervised Vehicle Re-identification with Progressive Adaptation

Jinjia Peng, Yang Wang, Huibing Wang +3

Vehicle re-identification (reID) aims at identifying vehicles across different non-overlapping cameras views. The existing methods heavily relied on well-labeled datasets for ideal…

cs.CV20202 cited

Convolutional Dictionary Pair Learning Network for Image Representation Learning

Zhao Zhang, Yulin Sun, Yang Wang +3

Both the Dictionary Learning (DL) and Convolutional Neural Networks (CNN) are powerful image representation learning systems based on different mechanisms and principles, however w…

cs.CV20207 cited

Multilayer Collaborative Low-Rank Coding Network for Robust Deep Subspace Discovery

Xianzhen Li, Zhao Zhang, Yang Wang +3

For subspace recovery, most existing low-rank representation (LRR) models performs in the original space in single-layer mode. As such, the deep hierarchical information cannot be…

cs.CV20207 cited

Fully-Convolutional Intensive Feature Flow Neural Network for Text Recognition

Zhao Zhang, Zemin Tang, Zheng Zhang +3

The Deep Convolutional Neural Networks (CNNs) have obtained a great success for pattern recognition, such as recognizing the texts in images. But existing CNNs based frameworks sti…

cs.CV2020

Semi-DerainGAN: A New Semi-supervised Single Image Deraining Network

Yanyan Wei, Zhao Zhang, Yang Wang +4

Removing the rain streaks from single image is still a challenging task, since the shapes and directions of rain streaks in the synthetic datasets are very different from real imag…

cs.CV2019

Compressed DenseNet for Lightweight Character Recognition

Zhao Zhang, Zemin Tang, Yang Wang +3

Convolutional Recurrent Neural Network (CRNN) is a popular network for recognizing texts in images. Advances like the variant of CRNN, such as Dense Convolutional Network with Conn…