Making Generalized Category Discovery Useful for Biodiversity Monitoring

By organizing previously unseen categories into coherent groups, Generalized Category Discovery (GCD) can potentially facilitate biodiversity monitoring by organizing observations of new species, enabling models to work with data-deficient species, and accelerating individual re-identification. Yet GCD methods are rarely designed to bear in mind the goals and constraints of biologists or the challenges posed by biodiversity data. This raises a practical question: how can we make GCD useful for biodiversity monitoring? To address this question, we formulate three biodiversity monitoring tasks that can benefit from GCD and propose practical metrics to evaluate their downstream performance under budget constraints. We introduce SciurusSciurus is the genus name of fox squirrels (Sciurus niger), which are known to organize their food caches by nut type delgado2017caching., a GCD-based sample selection framework for identifying informative samples, and show that it substantially improves downstream performance over existing sample selection methods. We further find that clustering accuracy, the most commonly used evaluation metric for GCD, is not always a reliable proxy for downstream utility, suggesting that selecting models based solely on clustering accuracy may be suboptimal for downstream applications. To address this gap, we introduce Homogeneity, a complementary metric that measures within-cluster consistency to better predict downstream performance. Together, our work provides a concrete first step toward making GCD useful for biodiversity monitoring in practice.