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Multimodal learning is an essential paradigm for addressing complexreal-world problems, where individual data modalities are typicallyinsufficient to accurately solve a given modelling task. While various deeplearning approaches have successfully addressed these challenges, theirreasoning process is often opaque; limiting the capabilities for a principledexplainable cross-modal analysis and any domain-expert intervention. In thispaper, we introduce SHARCS (SHARed Concept Space) -- a novel concept-basedapproach for explainable multimodal learning. SHARCS learns and mapsinterpretable concepts from different heterogeneous modalities into a singleunified concept-manifold, which leads to an intuitive projection ofsemantically similar cross-modal concepts. We demonstrate that such an approachcan lead to inherently explainable task predictions while also improvingdownstream predictive performance. Moreover, we show that SHARCS can operateand significantly outperform other approaches in practically significantscenarios, such as retrieval of missing modalities and cross-modalexplanations. Our approach is model-agnostic and easily applicable to differenttypes (and number) of modalities, thus advancing the development of effective,interpretable, and trustworthy multimodal approaches.

SHARCS: Shared Concept Space for Explainable Multimodal Learning