WildMatch: weakly supervised matcher adaptation boosts wildlife re-identification accuracy
Turhan Can Kargin · hf · 2026-10-07
WildMatch adapts a pretrained keypoint matcher for wildlife re-identification using only identity labels — no keypoint or geometric correspondence ground truth required.
- It mines informative image pairs with the off-the-shelf matcher, derives weak positive/negative supervision from identity agreement, and contrastively fine-tunes the network to strengthen same-identity correspondences.
- It beats off-the-shelf matchers and a state-of-the-art local–global fusion method across open-source wildlife datasets.
- Under an open-world protocol with held-out individuals, it learns a transferable correspondence prior rather than memorizing identities.
- Claimed as the first matcher-level, identity-supervised adaptation study for animal re-identification, enabling data-efficient specialization using annotations already present in monitoring datasets.
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