MANGO: Multimodal Attention-based Normalizing Flow Approach to Fusion Learning

• URL: http://arxiv.org/abs/2508.10133v1
• Date: Wed, 13 Aug 2025 18:56:57 GMT
• Title: MANGO: Multimodal Attention-based Normalizing Flow Approach to Fusion Learning
• Authors: Thanh-Dat Truong, Christophe Bobda, Nitin Agarwal, Khoa Luu,
• Abstract summary: This paper introduces a novel Multimodal Attention-based Normalizing Flow (MANGO) approach.<n>We propose a new Invertible Cross-Attention layer to develop the Normalizing Flow-based Model for multimodal data.<n>We also introduce three new cross-attention mechanisms: Modality-to-Modality Cross-Attention (MMCA), Inter-Modality Cross-Attention (IMCA), and Learnable Inter-Modality Cross-Attention (LICA)
• Score: 12.821814562210632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal learning has gained much success in recent years. However, current multimodal fusion methods adopt the attention mechanism of Transformers to implicitly learn the underlying correlation of multimodal features. As a result, the multimodal model cannot capture the essential features of each modality, making it difficult to comprehend complex structures and correlations of multimodal inputs. This paper introduces a novel Multimodal Attention-based Normalizing Flow (MANGO) approach\footnote{The source code of this work will be publicly available.} to developing explicit, interpretable, and tractable multimodal fusion learning. In particular, we propose a new Invertible Cross-Attention (ICA) layer to develop the Normalizing Flow-based Model for multimodal data. To efficiently capture the complex, underlying correlations in multimodal data in our proposed invertible cross-attention layer, we propose three new cross-attention mechanisms: Modality-to-Modality Cross-Attention (MMCA), Inter-Modality Cross-Attention (IMCA), and Learnable Inter-Modality Cross-Attention (LICA). Finally, we introduce a new Multimodal Attention-based Normalizing Flow to enable the scalability of our proposed method to high-dimensional multimodal data. Our experimental results on three different multimodal learning tasks, i.e., semantic segmentation, image-to-image translation, and movie genre classification, have illustrated the state-of-the-art (SoTA) performance of the proposed approach.

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