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
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Datasets
Market-1501
DukeMTMC-reID
MSMT17
MARS
DukeMTMC-VideoReID
iLIDS-VID
Latest papers with no code
Adaptive Intra-Class Variation Contrastive Learning for Unsupervised Person Re-Identification
The memory dictionary-based contrastive learning method has achieved remarkable results in the field of unsupervised person Re-ID.
Spatial Cascaded Clustering and Weighted Memory for Unsupervised Person Re-identification
We introduce the Spatial Cascaded Clustering and Weighted Memory (SCWM) method to address these challenges.
Population-Based Evolutionary Gaming for Unsupervised Person Re-identification
Extensive experiments demonstrate that (1) CRS approximately measures the performance of models without labeled samples; (2) and PEG produces new state-of-the-art accuracy for person re-identification, indicating the great potential of population-based network cooperative training for unsupervised learning.
Pseudo Labels Refinement with Intra-camera Similarity for Unsupervised Person Re-identification
Unsupervised person re-identification (Re-ID) aims to retrieve person images across cameras without any identity labels.
Discrepant and Multi-Instance Proxies for Unsupervised Person Re-Identification
To completely and accurately represent the information contained in a cluster and learn discriminative features, we propose to maintain discrepant cluster proxies and multi-instance proxies for a cluster.
Neighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification
In this paper, we propose a Neighbour Consistency guided Pseudo Label Refinement (NCPLR) framework, which can be regarded as a transductive form of label propagation under the assumption that the prediction of each example should be similar to its nearest neighbours'.
Confidence-guided Centroids for Unsupervised Person Re-Identification
Since samples with high confidence are exclusively involved in the formation of centroids, the identity information of low-confidence samples, i. e., boundary samples, are NOT likely to contribute to the corresponding centroid.
Dual Clustering Co-teaching with Consistent Sample Mining for Unsupervised Person Re-Identification
However, training two networks with a set of noisy pseudo labels reduces the complementarity of the two networks and results in label noise accumulation.
Domain Camera Adaptation and Collaborative Multiple Feature Clustering for Unsupervised Person Re-ID
In this paper, we aim at finding better feature representations on the unseen target domain from two aspects, 1) performing unsupervised domain adaptation on the labeled source domain and 2) mining potential similarities on the unlabeled target domain.
Pseudo-Pair based Self-Similarity Learning for Unsupervised Person Re-identification
In this paper, we present a pseudo-pair based self-similarity learning approach for unsupervised person re-ID without human annotations.
