activity
20182026
most citedNTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results

23 citations · 58 across the 23 of their papers we have counts for

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Showing 2019Show all

12 papers · 1 filter

cs.CV2019★ 4 cited

Eliminating cross-camera bias for vehicle re-identification

Jinjia Peng, Guangqi Jiang, Dongyan Chen +3

Vehicle re-identification (reID) often requires recognize a target vehicle in large datasets captured from multi-cameras. It plays an important role in the automatic analysis of th…

cs.CV2019

Graph-based Multi-view Binary Learning for Image Clustering

Guangqi Jiang, Huibing Wang, Jinjia Peng +2

Hashing techniques, also known as binary code learning, have recently gained increasing attention in large-scale data analysis and storage. Generally, most existing hash clustering…

cs.LG2019

Kernelized Multiview Subspace Analysis by Self-weighted Learning

Huibing Wang, Yang Wang, Zhao Zhang +4

With the popularity of multimedia technology, information is always represented or transmitted from multiple views. Most of the existing algorithms are graph-based ones to learn th…

cs.LG2019

A Multi-view Dimensionality Reduction Algorithm Based on Smooth Representation Model

Haohao Li, Huibing Wang

Over the past few decades, we have witnessed a large family of algorithms that have been designed to provide different solutions to the problem of dimensionality reduction (DR). Th…

cs.CV2019★ 4 cited

Cross Domain Knowledge Learning with Dual-branch Adversarial Network for Vehicle Re-identification

Jinjia Peng, Huibing Wang, Xianping Fu

The widespread popularization of vehicles has facilitated all people's life during the last decades. However, the emergence of a large number of vehicles poses the critical but cha…

cs.CV2019

Co-regularized Multi-view Sparse Reconstruction Embedding for Dimension Reduction

Huibing Wang, Jinjia Peng, Xianping Fu

With the development of information technology, we have witnessed an age of data explosion which produces a large variety of data filled with redundant information. Because dimensi…