5 citations · 12 across the 4 of their papers we have counts for
7 papers
Towards Fewer Labels: Support Pair Active Learning for Person Re-identification
Dapeng Jin, Minxian Li
Supervised-learning based person re-identification (re-id) require a large amount of manual labeled data, which is not applicable in practical re-id deployment. In this work, we pr…
Unsupervised Noisy Tracklet Person Re-identification
Minxian Li, Xiatian Zhu, Shaogang Gong
Existing person re-identification (re-id) methods mostly rely on supervised model learning from a large set of person identity labelled training data per domain. This limits their…
Intra-Camera Supervised Person Re-Identification
Xiangping Zhu, Xiatian Zhu, Minxian Li +3
Existing person re-identification (re-id) methods mostly exploit a large set of cross-camera identity labelled training data. This requires a tedious data collection and annotation…
Part-based Multi-stream Model for Vehicle Searching
Ya Sun, Minxian Li, Jianfeng Lu
Due to the enormous requirement in public security and intelligent transportation system, searching an identical vehicle has become more and more important. Current studies usually…
Intra-Camera Supervised Person Re-Identification: A New Benchmark
Xiangping Zhu, Xiatian Zhu, Minxian Li +2
Existing person re-identification (re-id) methods rely mostly on a large set of inter-camera identity labelled training data, requiring a tedious data collection and annotation pro…
Unsupervised Tracklet Person Re-Identification
Minxian Li, Xiatian Zhu, Shaogang Gong
Most existing person re-identification (re-id) methods rely on supervised model learning on per-camera-pair manually labelled pairwise training data. This leads to poor scalability…