用视觉提示统一检测与识别，提升野生动物细粒度辨识效果
Visual-Prompt Guided Wildlife Instance-Level Recognition
- 单阶段端到端模型，在潜在空间中直接搜索身份
- 在Wildlife-100数据集上达到30.584%的mAP，接近两阶段方法的70%
- 适合需要快速部署的野生动物监测场景
细粒度野生动物重识别仍是研究难点。现有最优方法采用检测与重识别分步流程。本文提出一种单阶段端到端检测与重识别模型，可在潜在空间中进行身份搜索。采用DINOv2获取鲁棒的空间几何特征，MegaDescriptor用于野生动物重识别。通过提示增强潜在查询特征，检测解码器在场景潜在空间中定位目标身份边界。初步结果表明，该模型在Wildlife-100数据集上取得30.584%的平均精度均值，相较于当前最优两阶段方法的44.89%仍具竞争力。定性结果展示出对动物身份的有效框选与识别。
原文摘要 · Abstract (English)
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.
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