Visual-Prompt Guided Wildlife Instance-Level Recognition

2026-08-18

AI Summary

本文针对细粒度野生动物重识别问题，提出了一种端到端的检测与重识别模型，采用DINOv2和MegaDescriptor增强潜在查询特征。

Abstract

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.

Problem

• wildlife re-identification
• fine-grained
• latent space

Innovation

• one-stage end-to-end model
• latent space identity searching
• DINOv2
• MegaDescriptor
• prompt re-identification features