发表机构
University of Johannesburg; Linköping University（约翰内斯堡大学; 林雪平大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对细粒度野生动物重识别挑战，提出单阶段端到端检测与重识别模型，采用DINOv2、MegaDescriptor及提示增强技术，在mAP指标上取得与两阶段方法相近的竞争力表现。
AI 中文摘要
细粒度野生动物重识别仍是研究中具有挑战性的领域。当前最先进的方法采用检测与重识别的流水线。我们提出一种单阶段端到端检测与重识别模型，该模型在潜在空间中执行身份搜索。我们采用DINOv2实现鲁棒的空间几何建模，采用MegaDescriptor进行野生动物重识别，通过提示重识别特征增强潜在查询，检测解码器查询场景潜在空间以建立目标身份周围的对象边界。初步结果显示，与最先进的两阶段方法（mAP为44.89%）相比，本模型的平均精度均值（mAP）为30.584%，表现具有竞争力；定性结果表明其能有效定位动物身份的边界并完成识别。
英文摘要
Fine-grained wildlife re-identification remains a challenging area in research. Current state-of-the-art approaches apply a detection and re-identification pipeline. We propose a one-stage end-to-end detection and re-identification model that performs identity searching within the latent space. We adopt DINOv2 for robust spatial geometry and MegaDescriptor for wildlife re-identification. We enhance latent queries with prompt re-identification features. A detection decoder queries the scene latent space to establish object boundaries around the target identity. Preliminary findings reflect a competitive mean average precision score of 30.584% compared to the state-of-the-art two stage approach of 44.89%. Qualitative results depict effective bounding and identification of animal identities.
CommentsAccepetd in ECCV Instance-Level Recognition and Generation Workshop 2026, Malmö Sweden
