433 citations · 806 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
Where-and-When to Look: Deep Siamese Attention Networks for Video-based Person Re-identification
Lin Wu, Yang Wang, Junbin Gao +1
Video-based person re-identification (re-id) is a central application in surveillance systems with significant concern in security. Matching persons across disjoint camera views in…
Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification
Lin Wu, Yang Wang, Junbin Gao +1
Person re-identification (re-ID) requires rapid, flexible yet discriminant representations to quickly generalize to unseen observations on-the-fly and recognize the same identity a…
Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval
Lin Wu, Yang Wang, Ling Shao
In this paper, we propose a novel deep generative approach to cross-modal retrieval to learn hash functions in the absence of paired training samples through the cycle consistency…
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…