36 citations · 92 across the 9 of their papers we have counts for
9 papers
Responsible Active Learning via Human-in-the-loop Peer Study
Yu-Tong Cao, Jingya Wang, Baosheng Yu +1
Active learning has been proposed to reduce data annotation efforts by only manually labelling representative data samples for training. Meanwhile, recent active learning applicati…
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…
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…
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…
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…
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…
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.…
Attribute Recognition by Joint Recurrent Learning of Context and Correlation
Jingya Wang, Xiatian Zhu, Shaogang Gong +1
Recognising semantic pedestrian attributes in surveillance images is a challenging task for computer vision, particularly when the imaging quality is poor with complex background c…
Discovering Visual Concept Structure with Sparse and Incomplete Tags
Jingya Wang, Xiatian Zhu, Shaogang Gong
Discovering automatically the semantic structure of tagged visual data (e.g. web videos and images) is important for visual data analysis and interpretation, enabling the machine i…