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

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

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IEEE ICASSP 2020 Virtual Conference May 2020

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

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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