发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对多物种动物重新识别问题，提出将其作为物种感知图构建，通过整合预处理、检索、验证、评分、边缘接纳和社区检测等流程，提升识别效果，所选提交在230个团队中排名第五，凸显视觉表示与多种约束校准整合的重要性。
AI 中文摘要
自动个体动物重新识别对于大规模生物多样性监测至关重要。然而，野外图像使得从姿态、光照、背景、分辨率和物种特定形态的干扰变化中分离身份线索变得复杂。DS@GT ARC提交给2026年动物CLEF的论文介绍了一种多物种图像聚类系统，用于重新识别欧亚猞猁、火蝾螈、蠵龟和德州角蜥。该方法将重新识别表述为对候选图像对进行物种感知图构建，而不是依赖单个描述符或最近邻检索。其流程整合了定制预处理、全局候选检索、基于LightGlue的多关键点家族局部验证、LightGBM对评分、保守边缘接纳和莱顿社区检测。跨物种的消融研究表明，局部特征支持、前景感知预处理和物种特定主干选择增强了对证据，而图操作点决定了碎片化和过度合并之间的权衡。所选提交在230个团队中排名第五，公开ARI为0.733，私有ARI为0.674。这些结果表明，强大的野生动物重新识别不仅需要强大的视觉表示，还需要对全局相似性、局部身份标记、邻域上下文和图级约束进行校准整合。代码可在该https URL找到。
英文摘要
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.
Comments18 pages, 8 figures. Accepted to the CLEF 2026 Working Notes. Code: https://github.com/dsgt-arc/animalclef-2026
