SheafAlign: A Sheaf-theoretic Framework for Decentralized Multimodal Alignment
This work addresses the challenge of decentralized multimodal alignment for applications like distributed sensing, offering a novel approach that is not incremental but provides specific improvements in efficiency and robustness.
The paper tackled the problem of multimodal alignment in decentralized settings where conventional methods fail due to the assumption of mutual redundancy across all modalities, and it resulted in SheafAlign, a framework that achieved superior zero-shot generalization, cross-modal alignment, and robustness to missing modalities with 50% lower communication cost than state-of-the-art baselines.
Conventional multimodal alignment methods assume mutual redundancy across all modalities, an assumption that fails in real-world distributed scenarios. We propose SheafAlign, a sheaf-theoretic framework for decentralized multimodal alignment that replaces single-space alignment with multiple comparison spaces. This approach models pairwise modality relations through sheaf structures and leverages decentralized contrastive learning-based objectives for training. SheafAlign overcomes the limitations of prior methods by not requiring mutual redundancy among all modalities, preserving both shared and unique information. Experiments on multimodal sensing datasets show superior zero-shot generalization, cross-modal alignment, and robustness to missing modalities, with 50\% lower communication cost than state-of-the-art baselines.