LoME: LoRA-Driven Multimodal Extractor for RGB-X Vision Tasks

RGB-X multimodal vision tasks present a highly promising approach to enhancing model performance in complex visual conditions. Existing multimodal frameworks are based on either the symmetric parallel network of feature fusion or the shared network of input fusion. However, parallel networks suffer from uncontrollable parameters and imbalanced optimization across modal branches, while shared networks often lead to a lack of diversity in gradient optimization. To address these challenges, we propose the LoRA-driven Multimodal Extractor (LoME), following a comprehensive analysis of existing multimodal frameworks. The low-rank properties of modal adapters for LoME ensure controllable growth in model parameters as the number of modalities increases. The dynamic parameter fusion between adapters and the shared feature extractor decouples gradient optimization directions, effectively mitigating imbalances caused by multimodal data biases while preserving complementary features. Moreover, we employ a training strategy based on dynamic rank allocation to reduce computational overhead and enhance modal diversity expression. We validate the effectiveness and generalizability of LoME across three multimodal vision tasks. LoME achieves superior performance compared to previous state-of-the-art methods on multiple datasets. For example, on the DroneVehicle dataset, our method achieves a 10.4% improvement in accuracy compared to the SOTA method, while the parameter overhead is reduced to 23% of the previous network (44.63M). The code has been open-sourced at https://github.com/zyszxhy/LoME.