Unsupervised Vehicle Re-Identification
8 papers with code • 1 benchmarks • 1 datasets
Most implemented papers
Unsupervised Vehicle Re-Identification via Self-supervised Metric Learning using Feature Dictionary
The results of DPLM are applied to dictionary-based triplet loss (DTL) to improve the discriminativeness of learnt features and to refine the quality of the results of DPLM progressively.
Camera-Tracklet-Aware Contrastive Learning for Unsupervised Vehicle Re-Identification
However, this achievement requires large-scale and well-annotated datasets.
Part-based Pseudo Label Refinement for Unsupervised Person Re-identification
In this paper, we propose a novel Part-based Pseudo Label Refinement (PPLR) framework that reduces the label noise by employing the complementary relationship between global and part features.
Triplet Contrastive Representation Learning for Unsupervised Vehicle Re-identification
To address this problem, in this paper, we propose a simple Triplet Contrastive Representation Learning (TCRL) framework which leverages cluster features to bridge the part features and global features for unsupervised vehicle re-identification.
ConMAE: Contour Guided MAE for Unsupervised Vehicle Re-Identification
With the large-scale and dynamic road environment, the paradigm of supervised vehicle re-identification shows limited scalability because of the heavy reliance on large-scale annotated datasets.
Multi‑camera trajectory matching based on hierarchical clustering and constraints
The fast improvement of deep learning methods resulted in breakthroughs in image classification, object detection, and object tracking.
Prototypical Contrastive Learning-based CLIP Fine-tuning for Object Re-identification
Although prompt learning has enabled a recent work named CLIP-ReID to achieve promising performance, the underlying mechanisms and the necessity of prompt learning remain unclear due to the absence of semantic labels in ReID tasks.
CA-Jaccard: Camera-aware Jaccard Distance for Person Re-identification
In particular, Jaccard distance calculates the distance based on the overlap of relevant neighbors.