Incomplete Modalities Restoration via Hierarchical Adaptation for Robust Multimodal Segmentation

Multimodal semantic segmentation has significantly advanced the field of semantic segmentation by integrating data from multiple sources. However, this task often encounters missing modality scenarios due to challenges such as sensor failures or data transmission errors, which can result in substantial performance degradation. Existing approaches to addressing missing modalities predominantly involve training separate models tailored to specific missing scenarios, typically requiring considerable computational resources. In this paper, we propose a Hierarchical Adaptation framework to Restore Missing Modalities for Multimodal segmentation (HARM3), which enables frozen pretrained multimodal models to be directly applied to missing-modality semantic segmentation tasks with minimal parameter updates. Central to HARM3 is a text-instructed missing modality prompt module, which learns multimodal semantic knowledge by utilizing available modalities and textual instructions to generate prompts for the missing modalities. By incorporating a small set of trainable parameters, this module effectively facilitates knowledge transfer between high-resource domains and low-resource domains where missing modalities are more prevalent. Besides, to further enhance the model's robustness and adaptability, we introduce adaptive perturbation training and an affine modality adapter. Extensive experimental results demonstrate the effectiveness and robustness of HARM3 across a variety of missing modality scenarios.