AlignMamba: Enhancing Multimodal Mamba with Local and Global Cross-modal Alignment

• URL: http://arxiv.org/abs/2412.00833v1
• Date: Sun, 01 Dec 2024 14:47:41 GMT
• Title: AlignMamba: Enhancing Multimodal Mamba with Local and Global Cross-modal Alignment
• Authors: Yan Li, Yifei Xing, Xiangyuan Lan, Xin Li, Haifeng Chen, Dongmei Jiang,
• Abstract summary: Cross-modal alignment is crucial for multimodal representation fusion. We propose AlignMamba, an efficient and effective method for multimodal fusion. Experiments on complete and incomplete multimodal fusion tasks demonstrate the effectiveness and efficiency of the proposed method.
• Score: 37.213291617683325
• License:
• Abstract: Cross-modal alignment is crucial for multimodal representation fusion due to the inherent heterogeneity between modalities. While Transformer-based methods have shown promising results in modeling inter-modal relationships, their quadratic computational complexity limits their applicability to long-sequence or large-scale data. Although recent Mamba-based approaches achieve linear complexity, their sequential scanning mechanism poses fundamental challenges in comprehensively modeling cross-modal relationships. To address this limitation, we propose AlignMamba, an efficient and effective method for multimodal fusion. Specifically, grounded in Optimal Transport, we introduce a local cross-modal alignment module that explicitly learns token-level correspondences between different modalities. Moreover, we propose a global cross-modal alignment loss based on Maximum Mean Discrepancy to implicitly enforce the consistency between different modal distributions. Finally, the unimodal representations after local and global alignment are passed to the Mamba backbone for further cross-modal interaction and multimodal fusion. Extensive experiments on complete and incomplete multimodal fusion tasks demonstrate the effectiveness and efficiency of the proposed method.

Related papers

• Enhancing Unimodal Latent Representations in Multimodal VAEs through Iterative Amortized Inference [20.761803725098005]
  Multimodal variational autoencoders (VAEs) aim to capture shared latent representations by integrating information from different data modalities. A significant challenge is accurately inferring representations from any subset of modalities without training an impractical number of inference networks for all possible modality combinations. We introduce multimodal iterative amortized inference, an iterative refinement mechanism within the multimodal VAE framework.
  arXiv  Detail & Related papers  (2024-10-15T08:49:38Z)
• How Intermodal Interaction Affects the Performance of Deep Multimodal Fusion for Mixed-Type Time Series [3.6958071416494414]
  Mixed-type time series (MTTS) is a bimodal data type common in many domains, such as healthcare, finance, environmental monitoring, and social media. The integration of both modalities through multimodal fusion is a promising approach for processing MTTS. We present a comprehensive evaluation of several deep multimodal fusion approaches for MTTS forecasting.
  arXiv  Detail & Related papers  (2024-06-21T12:26:48Z)
• Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modality [31.727012729846333]
  Multimodal Federated Cross Prototype Learning (MFCPL) is a novel approach for MFL under severely missing modalities. MFCPL provides diverse modality knowledge in modality-shared level with the cross-modal regularization and modality-specific level with cross-modal contrastive mechanism. Our approach introduces the cross-modal alignment to provide regularization for modality-specific features, thereby enhancing overall performance.
  arXiv  Detail & Related papers  (2024-01-25T02:25:23Z)
• Multimodal Representation Learning by Alternating Unimodal Adaptation [73.15829571740866]
  We propose MLA (Multimodal Learning with Alternating Unimodal Adaptation) to overcome challenges where some modalities appear more dominant than others during multimodal learning. MLA reframes the conventional joint multimodal learning process by transforming it into an alternating unimodal learning process. It captures cross-modal interactions through a shared head, which undergoes continuous optimization across different modalities. Experiments are conducted on five diverse datasets, encompassing scenarios with complete modalities and scenarios with missing modalities.
  arXiv  Detail & Related papers  (2023-11-17T18:57:40Z)
• Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding [62.70450216120704]
  Unsupervised pre-training has shown great success in skeleton-based action understanding. We propose a Unified Multimodal Unsupervised Representation Learning framework, called UmURL. UmURL exploits an efficient early-fusion strategy to jointly encode the multi-modal features in a single-stream manner.
  arXiv  Detail & Related papers  (2023-11-06T13:56:57Z)
• Deep Equilibrium Multimodal Fusion [88.04713412107947]
  Multimodal fusion integrates the complementary information present in multiple modalities and has gained much attention recently. We propose a novel deep equilibrium (DEQ) method towards multimodal fusion via seeking a fixed point of the dynamic multimodal fusion process. Experiments on BRCA, MM-IMDB, CMU-MOSI, SUN RGB-D, and VQA-v2 demonstrate the superiority of our DEQ fusion.
  arXiv  Detail & Related papers  (2023-06-29T03:02:20Z)
• Provable Dynamic Fusion for Low-Quality Multimodal Data [94.39538027450948]
  Dynamic multimodal fusion emerges as a promising learning paradigm. Despite its widespread use, theoretical justifications in this field are still notably lacking. This paper provides theoretical understandings to answer this question under a most popular multimodal fusion framework from the generalization perspective. A novel multimodal fusion framework termed Quality-aware Multimodal Fusion (QMF) is proposed, which can improve the performance in terms of classification accuracy and model robustness.
  arXiv  Detail & Related papers  (2023-06-03T08:32:35Z)
• Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos [58.93586436289648]
  We propose a multi-scale cooperative multimodal transformer (MCMulT) architecture for multimodal sentiment analysis. Our model outperforms existing approaches on unaligned multimodal sequences and has strong performance on aligned multimodal sequences.
  arXiv  Detail & Related papers  (2022-06-16T07:47:57Z)
• Multi-Modal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing [73.29587731448345]
  We propose a novel method, dubbed Cross-Modal Info-Max Hashing (CMIMH) We learn informative representations that can preserve both intra- and inter-modal similarities. The proposed method consistently outperforms other state-of-the-art cross-modal retrieval methods.
  arXiv  Detail & Related papers  (2021-12-13T08:58:03Z)
• Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis [16.32509144501822]
  We propose a framework named MultiModal InfoMax (MMIM), which hierarchically maximizes the Mutual Information (MI) in unimodal input pairs. The framework is jointly trained with the main task (MSA) to improve the performance of the downstream MSA task.
  arXiv  Detail & Related papers  (2021-09-01T14:45:16Z)
• Robust Latent Representations via Cross-Modal Translation and Alignment [36.67937514793215]
  Most multi-modal machine learning methods require that all the modalities used for training are also available for testing. To address this limitation, we aim to improve the testing performance of uni-modal systems using multiple modalities during training only. The proposed multi-modal training framework uses cross-modal translation and correlation-based latent space alignment.
  arXiv  Detail & Related papers  (2020-11-03T11:18:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.