ArticleThe Visual computer2022
A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets.
Article in The Visual computer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
44 citing papers in PubMed.
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- Knowledge-guided multimodal reasoning framework for intelligent power grid maintenance.Scientific reports · 2026Article
- Enhancing crop yield prediction accuracy with a novel interpretable deep learning model: MHCNN-LSTM-MHA.Scientific reports · 2026Article
- Improving the Robustness of Odour Recognition with Odour-Image Data Fusion in Open-Air Settings.Sensors (Basel, Switzerland) · 2026Article
- A novel interpretable machine learning framework integrating clinicopathological and radiomic features for early recurrence prediction in mass-forming intrahepatic cholangiocarcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- AI-driven biomimetic design: sustainable bio-materials for eco-friendly aesthetic solutions.Frontiers in bioengineering and biotechnology · 2026Article
- Ultrasound-guided structural fusion enhances the interpretability of electrical impedance tomography for layered tissue imaging.Journal of electrical bioimpedance · 2026Article
- HybridCATNet: an explainable CNN-Transformer framework with early multi-channel fusion and uncertainty estimation for pneumonia detection from chest X-rays.Frontiers in digital health · 2026Article
- Analysis of cognitive mechanisms in phoneme perception and pronunciation errors among Korean language learners.Frontiers in psychology · 2026Article
- Research on the impact of explosive martial arts training on emotion regulation and attention based on questionnaire data.Scientific reports · 2025Article
- UFOS-Net leverages small-scale feature fusion for diabetic foot ulcer segmentation.Scientific reports · 2025Article
- TOSD: A Hierarchical Object-Centric Descriptor Integrating Shape, Color, and Topology.Sensors (Basel, Switzerland) · 2025Article
- High-Accuracy Recognition Method for Diseased Chicken Feces Based on Image and Text Information Fusion.Animals : an open access journal from MDPI · 2025Article
- Video prediction based on temporal aggregation and recurrent propagation for surveillance videos.MethodsX · 2025Article
- An Integrated Navigation Method Based on the Strapdown Inertial Navigation System/Scene-Matching Navigation System for UAVs.Sensors (Basel, Switzerland) · 2025Article
- A machine learning approach for multimodal data fusion for survival prediction in cancer patients.NPJ precision oncology · 2025Article
- Novel pre-spatial data fusion deep learning approach for multimodal volumetric outcome prediction models in radiotherapy.Medical physics · 2025Article
- ModuCLIP: multi-scale CLIP framework for predicting foundation pit deformation in multi-modal robotic systems.Frontiers in neurorobotics · 2025Article
- Enhancing gastroenterology with multimodal learning: the role of large language model chatbots in digestive endoscopy.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The research progress in multimodal learning has grown rapidly over the last decade in several areas, especially in computer vision. The growing potential of multimodal data streams and deep learning algorithms has contributed to the increasing universality of deep multimodal learning. This involves the development of models capable of processing and analyzing the multimodal information uniformly. Unstructured real-world data can inherently take many forms, also known as modalities, often including visual and textual content. Extracting relevant patterns from this kind of data is still a motivating goal for researchers in deep learning. In this paper, we seek to improve the understanding of key concepts and algorithms of deep multimodal learning for the computer vision community by exploring how to generate deep models that consider the integration and combination of heterogeneous visual cues across sensory modalities. In particular, we summarize six perspectives from the current literature on deep multimodal learning, namely: multimodal data representation, multimodal fusion (i.e., both traditional and deep learning-based schemes), multitask learning, multimodal alignment, multimodal transfer learning, and zero-shot learning. We also survey current multimodal applications and present a collection of benchmark datasets for solving problems in various vision domains. Finally, we highlight the limitations and challenges of deep multimodal learning and provide insights and directions for future research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.