Visual-Prompt Guided Wildlife Instance-Level Recognition
arXiv:2608.18246v1 Announce Type: new 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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[Submitted on 18 Aug 2026]
Title:Visual-Prompt Guided Wildlife Instance-Level Recognition
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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.
Comments: Accepetd in ECCV Instance-Level Recognition and Generation Workshop 2026, Malmö Sweden
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2608.18246 [cs.CV]
(or arXiv:2608.18246v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.18246
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mufhumudzi Muthivhi [view email] [v1] Tue, 18 Aug 2026 18:48:25 UTC (317 KB)
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