activity
20172021
most citedAttribute Recognition by Joint Recurrent Learning of Context and Correlation

36 citations · 60 across the 5 of their papers we have counts for

collaborators

8 papers

cs.IR202118 cited

Embedding-based Recommender System for Job to Candidate Matching on Scale

Jing Zhao, Jingya Wang, Madhav Sigdel +4

The online recruitment matching system has been the core technology and service platform in CareerBuilder. One of the major challenges in an online recruitment scenario is to provi…

cs.CV20214 cited

Online Multiple Object Tracking with Cross-Task Synergy

Song Guo, Jingya Wang, Xinchao Wang +1

Modern online multiple object tracking (MOT) methods usually focus on two directions to improve tracking performance. One is to predict new positions in an incoming frame based on…

cs.CV20202 cited

Symbiotic Adversarial Learning for Attribute-based Person Search

Yu-Tong Cao, Jingya Wang, Dacheng Tao

Attribute-based person search is in significant demand for applications where no detected query images are available, such as identifying a criminal from witness. However, the task…

cs.CV2020

Pose-guided Visible Part Matching for Occluded Person ReID

Shang Gao, Jingya Wang, Huchuan Lu +1

Occluded person re-identification is a challenging task as the appearance varies substantially with various obstacles, especially in the crowd scenario. To address this issue, we p…

cs.IR2019

Tripartite Vector Representations for Better Job Recommendation

Mengshu Liu, Jingya Wang, Kareem Abdelfatah +1

Job recommendation is a crucial part of the online job recruitment business. To match the right person with the right job, a good representation of job postings is required. Such r…

cs.CV2018

Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification

Jingya Wang, Xiatian Zhu, Shaogang Gong +1

Most existing person re-identification (re-id) methods require supervised model learning from a separate large set of pairwise labelled training data for every single camera pair.…