DS@GT ARC at AnimalCLEF 2026: Species-Aware Graph Construction for Multi-Species Animal Re-Identification

2026-07-17

AI Summary

This work addresses the challenge of identity confusion in wild multi-species animal re-identification, which arises from variations in pose, illumination, background, resolution, and morphology. To tackle this issue, the authors propose a species-aware graph construction framework that integrates foreground-aware preprocessing, species-specific backbone networks, LightGlue for local feature matching, and LightGBM for pairwise scoring. Robust clustering is achieved through conservative edge insertion followed by Leiden community detection, effectively mitigating over-merging caused by bridging edges. The method was evaluated in the AnimalCLEF 2026 competition, where it ranked 5th among 230 teams, achieving an Adjusted Rand Index (ARI) of 0.733 on the public test set and 0.674 on the private test set, demonstrating its effectiveness and state-of-the-art performance.

Abstract

Automated individual animal re-identification is essential for large-scale biodiversity monitoring; however, field imagery complicates separating identity cues from nuisance variation in pose, illumination, background, resolution, and species-specific morphology. The DS@GT ARC submission to AnimalCLEF 2026 introduces a multi-species image-clustering system for re-identifying Eurasian lynx, fire salamanders, loggerhead sea turtles, and Texas horned lizards. Instead of relying on a single descriptor or nearest-neighbor retrieval, this approach formulates re-identification as species-aware graph construction over candidate image pairs. The pipeline integrates tailored preprocessing, global candidate retrieval, LightGlue-based local verification with multiple keypoint families, LightGBM pair scoring, conservative edge admission, and Leiden community detection. This design directly addresses a primary failure mode of clustering-based re-identification: high-scoring false pairs that act as bridge edges and merge distinct individuals through transitive closure. Across species, ablation studies demonstrate that local feature support, foreground-aware preprocessing, and species-specific backbone selection enhance pair evidence, while graph operating points determine the trade-off between fragmentation and over-merging. The selected submission achieved a public ARI of 0.733 and a private ARI of 0.674, ranking fifth among 230 teams. These results indicate that robust wildlife re-identification requires not only strong visual representations but also calibrated integration of global similarity, local identity markings, neighborhood context, and graph-level constraints. The code can be found at https://github.com/dsgt-arc/animalclef-2026.

Problem

• animal re-identification
• multi-species
• nuisance variation
• clustering
• identity cues

Innovation

• species-aware graph construction
• multi-species animal re-identification
• LightGlue-based local verification
• Leiden community detection
• bridge edge mitigation