Wildlife image matcher trained with weak identity labels improves animal spotting
WildMatch: Weakly Supervised Image Matcher Adaptation for Wildlife Re-Identification
Computer Vision and Pattern RecognitionArtificial IntelligenceInformation Retrieval
Summary
Identifying individual animals in camera trap photos is hard because animals have unique patterns that need careful matching. The authors show a method that improves existing image matching software by training it just using labels that say if two pictures are of the same animal or not, without detailed point-by-point matches. This approach works better than methods that rely on many labeled pictures or generic matchers not specialized for animals. It can learn to recognize new animals it hasn’t seen before, making it more useful for wildlife monitoring.
What this means in practice
- •For wildlife monitoring teams: Improve animal tracking by adapting image matchers using only known animal identities without detailed image annotations.
- •For conservation technology developers: Build tools that match individual animals across photos using limited labeled data, enhancing tracking and population counts.
Authors
Turhan Can Kargin, Piotr Kubaty, Ekaterina Rostovskaya, Izabela Wierzbowska, Bartosz Zieliński, Marcin Przewięźlikowski
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.