GenFuse: Cross-Sample Genetic Fusion for Incomplete Multimodal Training in Remote Sensing

Multimodal remote sensing classifiers typically assume complete training pairs and handle missing modalities only at inference, limiting their applicability to incomplete multimodal training (IMT), where modality absence also occurs during training. We propose GenFuse, a cross-sample genetic fusion framework that generates a missing-modality proxy by recombining two complementary parents rather than relying solely on intra-sample mapping. The current sample's available feature serves as the semantic parent, while features retrieved from the training bank corresponding to the missing modality are aggregated into the modality parent. Feature-wise crossover and range-scaled mutation with a learnable offset produce the proxy, which is fused with the available feature for classification. The complete subset supervises proxy alignment, while genetic consistency anchors proxies for incomplete samples to their available features without ground-truth features from the missing modality. Experiments on three datasets under specific and probabilistic modality-missing protocols demonstrate superior classification performance.