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аналитика - Unsupervised learning computer vision
Unsupervised Visible-Infrared Person Re-Identification via Pseudo-Label Correction and Modality-Level Alignment
The core message of this paper is to propose a novel unsupervised framework for visible-infrared person re-identification (VI-ReID) that addresses the challenges of noisy pseudo-labels and large modality gaps between visible and infrared images.
Adaptive Intra-Class Variation Contrastive Learning for Efficient and Robust Unsupervised Person Re-Identification
An adaptive intra-class variation contrastive learning algorithm for unsupervised person re-identification that selects appropriate samples and outliers to dynamically update the memory dictionary based on the current learning capability of the model, leading to improved performance and faster convergence.
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