MIAM: Modality Imbalance-Aware Masking for Multimodal Ecological Applications

Multimodal learning is crucial for ecological applications, which rely on heterogeneous data sources (e.g., satellite imagery, environmental time series, tabular predictors, bioacoustics) but often suffer from incomplete data across and within modalities (e.g., unavailable satellite images due to cloud cover, missing records in a time series). While data masking strategies have been used to improve robustness to missing data by exposing models to varying input subsets during training, existing approaches typically rely on static masking and inadequately explore the space of input combinations. As a result, they fail to address modality imbalance, a critical challenge in multimodal learning where dominant modalities hinder the optimization of others. To fill this gap, we introduce Modality Imbalance-Aware Masking (MIAM), a dynamic masking strategy that: (i) explores the full space of input combinations; (ii) prioritizes informative or challenging subsets; and (iii) adaptively increases the masking probability of dominant modalities based on their relative performance and learning dynamics. We evaluate MIAM on two key ecological datasets, GeoPlant and TaxaBench, with diverse modality configurations, and show that MIAM significantly improves robustness and predictive performance over previous masking strategies. In addition, MIAM supports fine-grained contribution analysis across and within modalities, revealing which variables, time segments, or image regions most strongly drive performance.