TaxaBind: A Unified Embedding Space for Ecological Applications

Abstract

We present TaxaBind a unified embedding space for characterizing any species of interest. TaxaBind is a multimodal embedding space across six modalities: ground-level images of species geographic location satellite image text audio and environmental features useful for solving ecological problems. To learn this joint embedding space we leverage ground-level images of species as a binding modality. We propose multimodal patching a technique for effectively distilling the knowledge from various modalities into the binding modality. We construct two large datasets for pretraining: iSatNat with species images and satellite images and iSoundNat with species images and audio. Additionally we introduce TaxaBench-8k a diverse multimodal dataset with six paired modalities for evaluating deep learning models on ecological tasks. Experiments with TaxaBind demonstrate its strong zero-shot and emergent capabilities on a range of tasks including species classification cross-model retrieval and audio classification. The datasets and models are made available at https://github.com/mvrl/TaxaBind.