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arXiv · 2608.18246

Visual-Prompt Guided Wildlife Instance-Level Recognition

Mufhumudzi Muthivhi·Jiahao Huo·Terence van Zyl·Fredrik Gustafsson
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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.

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Mufhumudzi Muthivhi, Jiahao Huo, Terence van Zyl, Fredrik Gustafsson. 2026-08-18. Visual-Prompt Guided Wildlife Instance-Level Recognition. https://arxiv.org/abs/2608.18246

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