发表机构
Made In Alexandria Artificial Intelligence Team; Faculty of Computer Science and Engineering, Alamein International University; Faculty of Computers and Data Science, Alexandria University; Faculty of Engineering, Alexandria University; Alexandria Higher Institute of Engineering and Technology; Moscow Pedagogical State University (MPGU University)（亚历山大制造人工智能团队; 阿拉曼国际大学计算机科学与工程学院; 亚历山大大学计算机与数据科学学院; 亚历山大大学工程学院; 亚历山大高等工程与技术学院; 莫斯科国立师范大学（MPGU大学））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对AnimalCLEF26的开放集动物重识别任务，提出结合物种感知预处理的校准全局-局部融合方法，经实验其集成模型取得公开ARI 0.72124等优异结果，优于基线模型。
AI 中文摘要
AnimalCLEF26面向发现导向的动物重识别任务，要求系统既需将查询图像关联到已知个体，又需通过正确聚类发现未知个体。本文针对欧亚猞猁、火蝾螈、红海龟和德州角蜥的图像，提出一种适用于该场景的相似度到聚类的流程。该方法先通过分割分离目标标本，再对猞猁、红海龟和蝾螈图像应用轻量的物种特异性预处理以增强与身份相关的视觉线索，而德州角蜥图像仅在分割后使用。随后通过WildFusion估计成对相似度，其过程是校准并结合MiewID全局描述子与两个局部匹配分支（ALIKED+LightGlue和DISK+LightGlue）。得到的查询-查询相似度经优化后，通过基于图的聚类转换为身份簇，而查询-数据库相似度用于将置信样本关联到已知身份。本文评估了无训练和微调的MiewID变体，包括Dynamic ArcFace和SphereFace2-Focal适配，并将它们组合成最终集成模型。所选集成模型大幅优于WildFusion基线，取得公开调整兰德指数（ARI）0.72124和私有ARI 0.70393，而更简单的“预处理后校准”变体取得私有ARI 0.71087。这些结果表明，结合物种感知预处理选择的校准全局-局部融合，在具有挑战性的野外条件和视觉变化下，对开放集野生动物重识别是有效的。实现代码可在GitHub获取。
英文摘要
AnimalCLEF26 addresses discovery-oriented animal re-identification, where systems must both attach query images to known individuals and discover unseen individuals by clustering them correctly. We present a similarity-to-clustering pipeline for this setting across Eurasian lynx, fire salamander, loggerhead sea turtle, and Texas horned lizard images. The method first isolates the target specimen using segmentation and then applies lightweight species-specific preprocessing for lynx, sea turtle, and salamander images to enhance identity-relevant visual cues, while Texas horned lizard images are used after segmentation only. Pairwise similarities are then estimated with WildFusion by calibrating and combining a MiewID global descriptor with two local matching branches, ALIKED + LightGlue and DISK + LightGlue. The resulting query-query similarities are refined and converted into identity clusters using graph-based clustering, while query-database similarities are used to attach confident samples to known identities. We evaluate training-free and fine-tuned MiewID variants, including Dynamic ArcFace and SphereFace2-Focal adaptations, and combine them in the final ensemble. Our selected ensemble substantially improves on the WildFusion baseline, achieving the best public ARI of 0.72124 and a private ARI of 0.70393, while a simpler preprocessing-before-calibration variant achieves the best private ARI of 0.71087. These results indicate that calibrated global-local fusion with species-aware preprocessing choices is effective for open-set wildlife re-identification under challenging field conditions and visual variation. The implementation code is available on GitHub.
