Learning to Purification for Unsupervised Person Re-identification. (arXiv:2204.09931v2 [cs.CV] UPDATED)

Web: http://arxiv.org/abs/2204.09931

Unsupervised person re-identification is a challenging and promising task in
computer vision. Nowadays unsupervised person re-identification methods have
achieved great progress by training with pseudo labels. However, how to purify
feature and label noise is less explicitly studied in the unsupervised manner.
To purify the feature, we take into account two types of additional features
from different local views to enrich the feature representation. The proposed
multi-view features are carefully integrated into our cluster contrast learning
to leverage more discriminative …

arxiv cv identification learning person unsupervised