Unsupervised Person Re-Identification Using Multi-Branch Feature Compensation Network And Link-Based Cluster Dissimilarity Metric

Feature extraction and label estimation are critical in unsupervised person re-identification (re-ID). Most previous works focus on acquiring high-layer semantic features and reckon without the lower-layer details lost in the learning process, which cause
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