WildFusion：基于校准相似度融合的个体动物识别
WildFusion: Individual Animal Identification with Calibrated Similarity Fusion
AI总结：
WildFusion通过校准融合深度评分和局部匹配相似度进行动物个体识别，在零样本设置下平均准确率达84.0%，显著优于现有技术。
摘要：
我们提出了一种新方法——WildFusion，用于广泛动物物种的个体识别。该方法融合深度评分（如MegaDescriptor或DINOv2）和局部匹配相似度（如LoFTR和LightGlue）来识别个体动物。全局和局部信息融合通过相似度评分校准来实现。在零样本设置下，仅依靠局部相似度评分，WildFusion在17个数据集上测得的平均准确率为76.2%。这优于训练集包含17个数据集中15个的最先进模型MegaDescriptor-L。如果应用特定于数据集的校准，平均准确率提高2.3个百分点。WildFusion结合局部和全局相似度评分，显著优于现有技术——平均准确率达到84.0%，提高8.5个百分点；平均相对误差下降35%。我们公开代码和预训练模型，以便在生态学和保护领域立即使用。
英文摘要：
We propose a new method - WildFusion - for individual identification of a broad range of animal species. The method fuses deep scores (e.g., MegaDescriptor or DINOv2) and local matching similarity (e.g., LoFTR and LightGlue) to identify individual animals. The global and local information fusion is facilitated by similarity score calibration. In a zero-shot setting, relying on local similarity score only, WildFusion achieved mean accuracy, measured on 17 datasets, of 76.2%. This is better than the state-of-the-art model, MegaDescriptor-L, whose training set included 15 of the 17 datasets. If a dataset-specific calibration is applied, mean accuracy increases by 2.3% percentage points. WildFusion, with both local and global similarity scores, outperforms the state-of-the-art significantly - mean accuracy reached 84.0%, an increase of 8.5 percentage points; the mean relative error drops by 35%. We make the code and pre-trained models publicly available5, enabling immediate use in ecology and conservation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
