most citedExploring the Quality of GAN Generated Images for Person Re-Identification

21 citations · 35 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV202121 cited

Exploring the Quality of GAN Generated Images for Person Re-Identification

Yiqi Jiang, Weihua Chen, Xiuyu Sun +3

Recently, GAN based method has demonstrated strong effectiveness in generating augmentation data for person re-identification (ReID), on account of its ability to bridge the gap be…

cs.CV20214 cited

Towards Discriminative Representation Learning for Unsupervised Person Re-identification

Takashi Isobe, Dong Li, Lu Tian +3

In this work, we address the problem of unsupervised domain adaptation for person re-ID where annotations are available for the source domain but not for target. Previous methods t…

cs.CV20211 cited

An Empirical Study of Vehicle Re-Identification on the AI City Challenge

Hao Luo, Weihua Chen, Xianzhe Xu +7

This paper introduces our solution for the Track2 in AI City Challenge 2021 (AICITY21). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synt…

cs.CV2021

City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones

Chong Liu, Yuqi Zhang, Hao Luo +6

Multi-Target Multi-Camera Tracking has a wide range of applications and is the basis for many advanced inferences and predictions. This paper describes our solution to the Track 3…

cs.CV20204 cited

1st Place Solution to VisDA-2020: Bias Elimination for Domain Adaptive Pedestrian Re-identification

Jianyang Gu, Hao Luo, Weihua Chen +6

This paper presents our proposed methods for domain adaptive pedestrian re-identification (Re-ID) task in Visual Domain Adaptation Challenge (VisDA-2020). Considering the large gap…

cs.CV20205 cited

Multi-Domain Learning and Identity Mining for Vehicle Re-Identification

Shuting He, Hao Luo, Weihua Chen +5

This paper introduces our solution for the Track2 in AI City Challenge 2020 (AICITY20). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synt…