WildMatch: Weakly Supervised Image Matcher Adaptation for Wildlife Re-Identification

2026-10-05

AI Summary

This study addresses the challenge of adapting pretrained image matchers to wildlife re-identification, where keypoint annotations are typically unavailable. To this end, we propose the first matcher-level adaptation method based on identity supervision. Building upon a pretrained keypoint correspondence model, our approach performs weakly supervised fine-tuning using only identity labels. By mining positive and negative sample pairs and incorporating contrastive learning to optimize feature correspondences, it achieves efficient domain transfer without requiring geometric ground truth. Experiments demonstrate that the proposed method significantly outperforms both general-purpose matchers and existing state-of-the-art fusion approaches in accuracy on public datasets, while exhibiting strong open-world generalization capabilities.

Abstract

Individual animal re-identification from camera-trap imagery is an instance retrieval problem central to non-invasive wildlife monitoring: a query image must retrieve the correct individual from a reference set of known animals. This requires computer vision models to recognize distinctive local patterns in fur, skin, or other visual markings. Current approaches either learn global embeddings as a classification problem, requiring many labeled images per individual while largely ignoring local evidence, or apply off-the-shelf, domain-agnostic image matchers. Although such matchers are pretrained on large and diverse image collections, adapting them to wildlife imagery is challenging because available datasets are small and lack correspondence-level annotations. We study weakly supervised adaptation of a pretrained keypoint matcher using only identity labels, without keypoint-level or geometric correspondence ground truth. We mine informative image pairs with the pretrained matcher, derive weak positive and negative supervision from identity agreement, and contrastively fine-tune the matching network to strengthen correspondences for same-identity pairs and suppress them for different identities. Across open-source wildlife re-identification datasets, our approach improves accuracy over off-the-shelf matchers and a state-of-the-art local--global fusion method. Under an open-world protocol with held-out individuals, it learns a transferable correspondence prior rather than memorizing training identities. To our knowledge, this is the first study of matcher-level, identity-supervised adaptation for animal re-identification. Our method enables data-efficient specialization of image matching models to wildlife domains using identity annotations already available in typical monitoring datasets.

Problem

• wildlife re-identification
• image matcher adaptation
• weakly supervised learning
• instance retrieval
• camera-trap imagery

Innovation

• Weakly Supervised Adaptation
• Keypoint Matcher
• Wildlife Re-Identification
• Contrastive Fine-tuning
• Open-world Protocol