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20182020
most citedJointly Adversarial Network to Wavelength Compensation and Dehazing of Underwater Images

15 citations · 42 across the 16 of their papers we have counts for

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Showing 2019Show all

11 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…

eess.IV2019★ 15 cited

Jointly Adversarial Network to Wavelength Compensation and Dehazing of Underwater Images

Xueyan Ding, Yafei Wang, Yang Yan +3

Severe color casts, low contrast and blurriness of underwater images caused by light absorption and scattering result in a difficult task for exploring underwater environments. Dif…

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…

cs.CV2019

Mask-guided Style Transfer Network for Purifying Real Images

Tongtong Zhao, Yuxiao Yan, Jinjia Peng +2

Recently, the progress of learning-by-synthesis has proposed a training model for synthetic images, which can effectively reduce the cost of human and material resources. However,…