activity
20182023
most cited1st Place Solution to ECCV-TAO-2020: Detect and Represent Any Object for Tracking

12 citations · 26 across the 8 of their papers we have counts for

collaborators

10 papers

cs.CV2023

Graph Convolution Based Efficient Re-Ranking for Visual Retrieval

Yuqi Zhang, Qi Qian, Hongsong Wang +3

Visual retrieval tasks such as image retrieval and person re-identification (Re-ID) aim at effectively and thoroughly searching images with similar content or the same identity. Af…

cs.CV20211 cited

2nd Place Solution to Google Landmark Retrieval 2021

Zhang Yuqi, Xu Xianzhe, Chen Weihua +4

This paper presents the 2nd place solution to the Google Landmark Retrieval 2021 Competition on Kaggle. The solution is based on a baseline with training tricks from person re-iden…

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.CV202112 cited

1st Place Solution to ECCV-TAO-2020: Detect and Represent Any Object for Tracking

Fei Du, Bo Xu, Jiasheng Tang +3

We extend the classical tracking-by-detection paradigm to this tracking-any-object task. Solid detection results are first extracted from TAO dataset. Some state-of-the-art techniq…

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…