WildMatch: Weakly Supervised Image Matcher Adaptation for Wildlife Re-Identification
WildMatch adapts pretrained image matchers for wildlife re-identification using only identity labels.
WildMatch adapts a pretrained keypoint matcher for camera-trap animal re-identification using only identity labels, without keypoint or geometric correspondence annotations. It mines image pairs with the pretrained matcher, derives weak positive and negative supervision from identity agreement, and contrastively fine-tunes correspondences. Across open-source wildlife datasets it 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.
- Adapts a keypoint matcher using identity labels only.
- Contrastive fine-tuning strengthens same-identity correspondences.
- Beats off-the-shelf matchers and a local-global fusion baseline.
- Open-world tests learn a transferable prior, not memorized identities.
From the original article
1 min read at huggingface.co
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.
By Turhan Can Kargin, Piotr Kubaty, Ekaterina Rostovskaya · huggingface.co
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