activity
20162019
most citedMulti-View Spectral Clustering via Structured Low-Rank Matrix Factorization

433 citations · 801 across the 7 of their papers we have counts for

collaborators

13 papers

cs.CV20194 cited

Deep Instance-Level Hard Negative Mining Model for Histopathology Images

Meng Li, Lin Wu, Arnold Wiliem +3

Histopathology image analysis can be considered as a Multiple instance learning (MIL) problem, where the whole slide histopathology image (WSI) is regarded as a bag of instances (i…

cs.CV2019

CORAL8: Concurrent Object Regression for Area Localization in Medical Image Panels

Sam Maksoud, Arnold Wiliem, Kun Zhao +3

This work tackles the problem of generating a medical report for multi-image panels. We apply our solution to the Renal Direct Immunofluorescence (RDIF) assay which requires a path…

cs.CV20195 cited

Cross-Entropy Adversarial View Adaptation for Person Re-identification

Lin Wu, Richang Hong, Yang Wang +1

Person re-identification (re-ID) is a task of matching pedestrians under disjoint camera views. To recognise paired snapshots, it has to cope with large cross-view variations cause…

cs.CV2019

Few-Shot Deep Adversarial Learning for Video-based Person Re-identification

Lin Wu, Yang Wang, Hongzhi Yin +2

Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages. Existing methods rely on supervision signals to…

cs.CV2018

3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-identification

Lin Wu, Yang Wang, Ling Shao +1

In this paper, we introduce a global video representation to video-based person re-identification (re-ID) that aggregates local 3D features across the entire video extent. Most of…

cs.CV20184 cited

Crossing Generative Adversarial Networks for Cross-View Person Re-identification

Chengyuan Zhang, Lin Wu, Yang Wang

Person re-identification (\textit{re-id}) refers to matching pedestrians across disjoint yet non-overlapping camera views. The most effective way to match these pedestrians underta…