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arXiv AI By Mufhumudzi Muthivhi, Jiahao Huo, Terence van Zyl, Fredrik Gustafsson

Visual-Prompt Guided Wildlife Instance-Level Recognition

Read the original on arXiv AI →

The paper introduces a one-stage end-to-end model for wildlife instance-level recognition that integrates detection and re-identification within a single latent space. It leverages DINOv2 for spatial geometry and MegaDescriptor for re-identification, while enhancing latent queries with prompt re-identification features. Preliminary results show a competitive mean average precision of 30.584% compared to the state-of-the-art two-stage approach of 44.89%, with qualitative evidence of effective bounding and identification of animal identities.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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