The paper proposes a Feature-Aware Noise Contrastive Learning (FANCL) method for unsupervised red panda re-identification. The key highlights are:
FANCL employs a Feature-Aware Noise Addition (FANA) module to generate noised images by adding noise to feature-aware regions of the original images. This encourages the model to learn more comprehensive and global features.
FANCL uses a dual-branch network framework, where one branch processes the original images and the other processes the noised images. It constructs a multi-cluster memory dictionary to maintain cluster-level features.
FANCL designs two contrastive learning losses: a cluster contrastive loss to enhance the discriminability between different clusters, and a consistency contrastive loss to narrow the gap between original and noised features.
Experiments on a red panda dataset show that FANCL outperforms several state-of-the-art unsupervised re-identification methods and achieves performance comparable to supervised methods, demonstrating the effectiveness of the proposed unsupervised approach.
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by Jincheng Zha... a las arxiv.org 05-02-2024
https://arxiv.org/pdf/2405.00468.pdfConsultas más profundas