DS@GT AnimalCLEF: Triplet Learning over ViT Manifolds with Nearest Neighbor Classification for Animal Re-identification

• URL: http://arxiv.org/abs/2509.12353v1
• Date: Mon, 15 Sep 2025 18:31:01 GMT
• Title: DS@GT AnimalCLEF: Triplet Learning over ViT Manifolds with Nearest Neighbor Classification for Animal Re-identification
• Authors: Anthony Miyaguchi, Chandrasekaran Maruthaiyannan, Charles R. Clark,
• Abstract summary: This paper details the DS@GT team's entry for the AnimalCLEF 2025 re-identification challenge.<n>We compare a general-purpose model (DINOv2) with a domain-specific model (MegaDescriptor) as a backbone.<n>We demonstrate that the general-purpose manifold is more difficult to reshape for fine-grained tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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