用ViT特征流形+近邻分类实现动物重识别，强调领域预训练的重要性。
DS@GT AnimalCLEF: Triplet Learning over ViT Manifolds with Nearest Neighbor Classification for Animal Re-identification
- 在ViT特征流形上做三元组学习，结合近邻分类识别动物个体。
- 专用模型MegaDescriptor提升0.13点，通用模型DINOv2仅提升0.03点。
- 证明通用特征难适应细粒度重识别，适合有领域数据的团队参考。
本文介绍DS@GT团队在AnimalCLEF 2025重识别挑战赛中的参赛方案。核心发现是后处理度量学习的效果高度依赖骨干网络嵌入的初始质量与领域相关性。对比通用模型DINOv2与领域专用模型MegaDescriptor作为骨干网络，采用带鲁棒阈值的K-近邻分类器识别已知个体或标记新个体。三元组学习投影头使专用模型性能提升0.13点，但对通用模型仅带来0.03点提升（平均BAKS和BAUS）。结果表明，通用特征流形难以重塑以适应细粒度任务，验证集损失停滞且可视化显示特征分布未改善。本工作揭示了用通用特征微调专用小样本重识别任务的局限性，强调领域预训练的关键作用。代码开源：github.com/dsgt-arc/animalclef-2025。
原文摘要 · Abstract (English)
This paper details the DS@GT team's entry for the AnimalCLEF 2025 re-identification challenge. Our key finding is that the effectiveness of post-hoc metric learning is highly contingent on the initial quality and domain-specificity of the backbone embeddings. We compare a general-purpose model (DINOv2) with a domain-specific model (MegaDescriptor) as a backbone. A K-Nearest Neighbor classifier with robust thresholding then identifies known individuals or flags new ones. While a triplet-learning projection head improved the performance of the specialized MegaDescriptor model by 0.13 points, it yielded minimal gains (0.03) for the general-purpose DINOv2 on averaged BAKS and BAUS. We demonstrate that the general-purpose manifold is more difficult to reshape for fine-grained tasks, as evidenced by stagnant validation loss and qualitative visualizations. This work highlights the critical limitations of refining general-purpose features for specialized, limited-data re-ID tasks and underscores the importance of domain-specific pre-training. The implementation for this work is publicly available at github.com/dsgt-arc/animalclef-2025.
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