[2411.17040] Multimodal Alignment and Fusion: A Survey
Nov 26, 2024 · This survey offers a comprehensive review of recent advancements in multimodal alignment and fusion within machine learning, spurred by the growing diversity of data types …
LANISTR: Multimodal learning from structured and unstructured data
May 22, 2024 · LANISTR is a new framework that enables multimodal learning by ingesting unstructured (image, text) and structured (time series, tabular) data, performing alignment and …
MultiJAF: Multi-modal joint entity alignment framework for multi-modal …
Aug 21, 2022 · In this paper, we propose a Multi-modal Joint entity Alignment Framework (MultiJAF), which can effectively utilize the knowledge of various modalities. Concretely, we …
Multimodal Representation Alignment for Image Generation: Text-Image …
Feb 27, 2025 · To mitigate the gap, we conducted preliminary experiments showing that large multimodal models (LMMs) offer an effective shared representation space, where image and …
Multimodal Alignment and Fusion: A Survey - arXiv.org
This survey offers a comprehensive review of recent advancements in multimodal alignment and fusion within machine learning, spurred by the growing diversity of data types such as text, …
A Multidisciplinary Multimodal Aligned Dataset for Academic Data ...
Jan 29, 2025 · To bridge this gap, we introduce a multidisciplinary multimodal aligned dataset (MMAD) specifically designed for academic data processing. This dataset encompasses over …
Multimodal Data Analytics: Importance in 2025
1 day ago · How Multimodal Data Analytics Works. The process of multimodal data analytics typically follows these steps: Data Collection: Gather input from various sources such as …
Multimodal Alignment and Fusion: A Survey | alphaXiv
Furthermore, this survey addresses the challenges of multimodal data integration - including alignment issues, noise resilience, and disparities in feature representation - while focusing on …
Multimodal Alignment and Fusion: A Survey
Nov 26, 2024 · Multimodal integration enables improved model accuracy and broader applicability by leveraging complementary information across different modalities, as well as facilitating …
Feature-based Alignment of Volumetric Multi-modal Images
The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage.
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