Multimodal Alignment and Fusion: A Survey

• URL: http://arxiv.org/abs/2411.17040v2
• Date: Sat, 11 Oct 2025 13:22:25 GMT
• Title: Multimodal Alignment and Fusion: A Survey
• Authors: Songtao Li, Hao Tang,
• Abstract summary: This survey provides a comprehensive overview of advances in multimodal alignment and fusion within the field of machine learning.<n>We systematically categorize and analyze key approaches to alignment and fusion through both structural perspectives.<n>This survey highlights critical challenges such as cross-modal misalignment, computational bottlenecks, data quality issues, and the modality gap.
• Score: 11.3029945633295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This survey provides a comprehensive overview of recent advances in multimodal alignment and fusion within the field of machine learning, driven by the increasing availability and diversity of data modalities such as text, images, audio, and video. Unlike previous surveys that often focus on specific modalities or limited fusion strategies, our work presents a structure-centric and method-driven framework that emphasizes generalizable techniques. We systematically categorize and analyze key approaches to alignment and fusion through both structural perspectives -- data-level, feature-level, and output-level fusion -- and methodological paradigms -- including statistical, kernel-based, graphical, generative, contrastive, attention-based, and large language model (LLM)-based methods, drawing insights from an extensive review of over 260 relevant studies. Furthermore, this survey highlights critical challenges such as cross-modal misalignment, computational bottlenecks, data quality issues, and the modality gap, along with recent efforts to address them. Applications ranging from social media analysis and medical imaging to emotion recognition and embodied AI are explored to illustrate the real-world impact of robust multimodal systems. The insights provided aim to guide future research toward optimizing multimodal learning systems for improved scalability, robustness, and generalizability across diverse domains.

Related papers

• Advanced Unsupervised Learning: A Comprehensive Overview of Multi-View Clustering Techniques [10.97758170701855]
  Multi-view clustering (MVC) is a class of unsupervised multi-view learning.<n>MVC compensates for the shortcomings of single-view methods.<n>The semantically rich nature of multi-view data increases its practical utility.
  arXiv  Detail & Related papers  (2025-12-04T16:32:02Z)
• Chunking Strategies for Multimodal AI Systems [0.0]
  This survey provides a comprehensive taxonomy and technical analysis of chunking strategies tailored for each modality.<n>We examine classical and modern approaches such as fixed-size token windowing, object-centric visual chunking, silence-based audio segmentation, and scene detection in videos.<n>We explore emerging cross-modal chunking strategies that aim to preserve alignment and semantic consistency across disparate data types.
  arXiv  Detail & Related papers  (2025-11-28T19:48:14Z)
• Survey of Multimodal Geospatial Foundation Models: Techniques, Applications, and Challenges [54.669838624278924]
  Foundation models have transformed natural language processing and computer vision.<n>With powerful generalization and transfer learning capabilities, they align naturally with the multimodal, multi-resolution, and multi-temporal characteristics of remote sensing data.<n>This survey delivers a comprehensive review of multimodal GFMs from a modality-driven perspective.
  arXiv  Detail & Related papers  (2025-10-27T03:40:00Z)
• Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models [0.0]
  This systematic literature review analyzes research published between January 2020 and early 2024 that focuses on the explainability of multimodal models.<n>We find that evaluation methods for XAI in multimodal settings are largely non-systematic, lacking consistency, robustness, and consideration for modality-specific cognitive and contextual factors.
  arXiv  Detail & Related papers  (2025-08-06T13:14:20Z)
• From Waveforms to Pixels: A Survey on Audio-Visual Segmentation [43.79010208565961]
  Audio-Visual aims to identify and segment sound-producing objects in videos by leveraging both visual and audio modalities.<n>We present a comprehensive overview of the AVS field, covering its problem formulation, benchmark datasets, evaluation metrics, and the progression of methodologies.
  arXiv  Detail & Related papers  (2025-07-29T22:20:51Z)
• Anomaly Detection and Generation with Diffusion Models: A Survey [51.61574868316922]
  Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing.<n>Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest.<n>This survey aims to guide researchers and practitioners in leveraging DMs for innovative AD solutions across diverse applications.
  arXiv  Detail & Related papers  (2025-06-11T03:29:18Z)
• Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision [49.073964142139495]
  We systematically review the applications and advancements of multimodal fusion methods and vision-language models.<n>For semantic scene understanding tasks, we categorize fusion approaches into encoder-decoder frameworks, attention-based architectures, and graph neural networks.<n>We identify key challenges in current research, including cross-modal alignment, efficient fusion, real-time deployment, and domain adaptation.
  arXiv  Detail & Related papers  (2025-04-03T10:53:07Z)
• Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey [124.23247710880008]
  multimodal CoT (MCoT) reasoning has recently garnered significant research attention.<n>Existing MCoT studies design various methodologies to address the challenges of image, video, speech, audio, 3D, and structured data.<n>We present the first systematic survey of MCoT reasoning, elucidating the relevant foundational concepts and definitions.
  arXiv  Detail & Related papers  (2025-03-16T18:39:13Z)
• Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation [2.549112678136113]
  Retrieval-Augmented Generation (RAG) mitigates issues by integrating external dynamic information enhancing factual and updated grounding. Cross-modal alignment and reasoning introduce unique challenges to Multimodal RAG, distinguishing it from traditional unimodal RAG. This survey lays the foundation for developing more capable and reliable AI systems.
  arXiv  Detail & Related papers  (2025-02-12T22:33:41Z)
• Survey of Large Multimodal Model Datasets, Application Categories and Taxonomy [2.294223504228228]
  Multimodal learning, a rapidly evolving field in artificial intelligence, seeks to construct more versatile and robust systems. Inspired by the human ability to assimilate information through many senses, this method enables applications such as text-to-video conversion, visual question answering, and image captioning. Recent developments in datasets that support multimodal language models (MLLMs) are highlighted in this overview.
  arXiv  Detail & Related papers  (2024-12-23T18:15:19Z)
• Multimodal Methods for Analyzing Learning and Training Environments: A Systematic Literature Review [3.0712840129998513]
  This literature review proposes a taxonomy and framework that encapsulates recent methodological advances in this field. We introduce a novel data fusion category -- mid fusion -- and a graph-based technique for refining literature reviews, termed citation graph pruning. There remains a need for further research to bridge the divide between multimodal learning and training studies and foundational AI research.
  arXiv  Detail & Related papers  (2024-08-22T22:42:23Z)
• Detecting Misinformation in Multimedia Content through Cross-Modal Entity Consistency: A Dual Learning Approach [10.376378437321437]
  We propose a Multimedia Misinformation Detection framework for detecting misinformation from video content by leveraging cross-modal entity consistency. Our results demonstrate that MultiMD outperforms state-of-the-art baseline models.
  arXiv  Detail & Related papers  (2024-08-16T16:14:36Z)
• HEMM: Holistic Evaluation of Multimodal Foundation Models [91.60364024897653]
  Multimodal foundation models can holistically process text alongside images, video, audio, and other sensory modalities. It is challenging to characterize and study progress in multimodal foundation models, given the range of possible modeling decisions, tasks, and domains.
  arXiv  Detail & Related papers  (2024-07-03T18:00:48Z)
• Multimodal Fusion on Low-quality Data: A Comprehensive Survey [110.22752954128738]
  This paper surveys the common challenges and recent advances of multimodal fusion in the wild. We identify four main challenges that are faced by multimodal fusion on low-quality data. This new taxonomy will enable researchers to understand the state of the field and identify several potential directions.
  arXiv  Detail & Related papers  (2024-04-27T07:22:28Z)
• Alternative Telescopic Displacement: An Efficient Multimodal Alignment Method [3.0903319879656084]
  This paper introduces an innovative approach to feature alignment that revolutionizes the fusion of multimodal information. Our method employs a novel iterative process of telescopic displacement and expansion of feature representations across different modalities, culminating in a coherent unified representation within a shared feature space.
  arXiv  Detail & Related papers  (2023-06-29T13:49:06Z)
• Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications [47.501121601856795]
  Multimodality Representation Learning is a technique of learning to embed information from different modalities and their correlations. Cross-modal interaction and complementary information from different modalities are crucial for advanced models to perform any multimodal task. This survey presents the literature on the evolution and enhancement of deep learning multimodal architectures.
  arXiv  Detail & Related papers  (2023-02-01T11:48:34Z)
• Vision+X: A Survey on Multimodal Learning in the Light of Data [64.03266872103835]
  multimodal machine learning that incorporates data from various sources has become an increasingly popular research area. We analyze the commonness and uniqueness of each data format mainly ranging from vision, audio, text, and motions. We investigate the existing literature on multimodal learning from both the representation learning and downstream application levels.
  arXiv  Detail & Related papers  (2022-10-05T13:14:57Z)
• Multimodal Image Synthesis and Editing: The Generative AI Era [131.9569600472503]
  multimodal image synthesis and editing has become a hot research topic in recent years. We comprehensively contextualize the advance of the recent multimodal image synthesis and editing. We describe benchmark datasets and evaluation metrics as well as corresponding experimental results.
  arXiv  Detail & Related papers  (2021-12-27T10:00:16Z)
• MultiBench: Multiscale Benchmarks for Multimodal Representation Learning [87.23266008930045]
  MultiBench is a systematic and unified benchmark spanning 15 datasets, 10 modalities, 20 prediction tasks, and 6 research areas. It provides an automated end-to-end machine learning pipeline that simplifies and standardizes data loading, experimental setup, and model evaluation. It introduces impactful challenges for future research, including robustness to large-scale multimodal datasets and robustness to realistic imperfections.
  arXiv  Detail & Related papers  (2021-07-15T17:54:36Z)
• A Variational Information Bottleneck Approach to Multi-Omics Data Integration [98.6475134630792]
  We propose a deep variational information bottleneck (IB) approach for incomplete multi-view observations. Our method applies the IB framework on marginal and joint representations of the observed views to focus on intra-view and inter-view interactions that are relevant for the target. Experiments on real-world datasets show that our method consistently achieves gain from data integration and outperforms state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2021-02-05T06:05:39Z)

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.