Abstract Object Re-identification (Re-ID) aims to identify specific objects across different
times and scenes, which is a widely researched task in computer vision. For a prolonged …

Deep transfer learning-based fault diagnosis of machines is achieved based on the
assumption that the source and target domain data could be centralized to assess the …

Abstract Neural Architecture Search (NAS) has been increasingly appealing to the society of
object Re-Identification (ReID), for that task-specific architectures significantly improve the …

Most existing unsupervised person re-identification (Re-ID) methods use clustering to
generate pseudo labels for model training. Unfortunately, clustering sometimes mixes …

Transfer learning (TL) utilizes data or knowledge from one or more source domains to
facilitate learning in a target domain. It is particularly useful when the target domain has very …

We present a novel unsupervised domain adaption method for person re-identification (reID)
that generalizes a model trained on a labeled source domain to an unlabeled target domain …

Gait recognition has emerged as a promising technique for the long-range retrieval of
pedestrians, providing numerous advantages such as accurate identification in challenging …

Heterogeneous pedestrian images are captured by visible and infrared cameras with
different spectrums, which play an important role in night-time video surveillance. However …

Unsupervised person re-identification is a challenging and promising task in computer
vision. Nowadays unsupervised person re-identification methods have achieved great …

Recently, clustering-based methods have been the dominant solution for unsupervised
person re-identification (ReID). Memory-based contrastive learning is widely used for its …