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Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis
As such, there has been a recent focus on unsupervised learning approaches to mitigate the data annotation issue; however, current approaches in literature have limited performance compared to supervised learning approaches as well as limited applicability for adoption in new environments.
Fairest of Them All: Establishing a Strong Baseline for Cross-Domain Person ReID
There has been recent interest in tackling this challenge using cross-domain approaches, which leverages data from source domains that are different than the target domain.
An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-Identification
In recent years, a variety of proposed methods based on deep convolutional neural networks (CNNs) have improved the state of the art for large-scale person re-identification (ReID).
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