Unsupervised Person Re-Identification
58 papers with code • 19 benchmarks • 11 datasets
Benchmarks
These leaderboards are used to track progress in Unsupervised Person Re-Identification
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Datasets
Market-1501
DukeMTMC-reID
MSMT17
MARS
DukeMTMC-VideoReID
iLIDS-VID
Latest papers with no code
Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions
Therefore, comprehensive surveys on this topic are essential to summarise challenges and solutions to foster future research.
Towards Discriminative Representation Learning for Unsupervised Person Re-identification
We observe that these proposed schemes are capable of facilitating the learning of discriminative feature representations.
Hard Samples Rectification for Unsupervised Cross-domain Person Re-identification
Person re-identification (re-ID) has received great success with the supervised learning methods.
Large-Scale Unsupervised Person Re-Identification with Contrastive Learning
In particular, most existing unsupervised and domain adaptation ReID methods utilize only the public datasets in their experiments, with labels removed.
Unsupervised Person Re-identification via Simultaneous Clustering and Consistency Learning
Unsupervised person re-identification (re-ID) has become an important topic due to its potential to resolve the scalability problem of supervised re-ID models.
Unsupervised Person Re-Identification with Multi-Label Learning Guided Self-Paced Clustering
The multi-label learning module leverages a memory feature bank and assigns each image with a multi-label vector based on the similarities between the image and feature bank.
Take More Positives: An Empirical Study of Contrastive Learing in Unsupervised Person Re-Identification
By studying two unsupervised person re-ID methods in a cross-method way, we point out a hard negative problem is handled implicitly by their designs of data augmentations and PK sampler respectively.
Fully Unsupervised Person Re-identification viaSelective Contrastive Learning
In this work, we propose a novel selective contrastive learning framework for unsupervised feature learning.
Global Distance-distributions Separation for Unsupervised Person Re-identification
To address this problem, we introduce a global distance-distributions separation (GDS) constraint over the two distributions to encourage the clear separation of positive and negative samples from a global view.
Unsupervised Person Re-identification via Multi-label Classification
Our label prediction and MMCL work iteratively and substantially boost the ReID performance.
