Results 61 to 70 of about 43,272 (165)

Enhancing emotion recognition through three modalities fusion and contrastive learning with the Mamba architecture

Complex & Intelligent Systems
Multimodal emotion recognition extracts emotional information from sequential multimodal data and classifies emotion tendencies. Current multimodal fusion methods based on artificial intelligence mainly rely on Transformers to extract features and ...
Qianjun Shuai, Xiaohao Chen, Feng Hu, Yongqiang Cheng   +3 more
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A fusion of social media context for efficient multimodal sentiment analysis using deep learning techniques

Discover Artificial Intelligence
The exponential growth of social media has generated vast amounts of multimodal data, including text, audio, and contextual information, offering valuable insights into user sentiments.
Satyendra Singh, Krishan Kumar, Brajesh Kumar   +2 more
doaj   +1 more source

A multiscale residual dense fusion network for nuclear medical image fusion

Intelligent Oncology
Introduction: Multimodal medical image fusion technology generates new images containing more accurate disease information by fusing different modal images.
Jun Fu, Gengyu Ge, Yihui Tan, Miaoqiang Yang, Qian Wang, Jie Yang, Ya Wang, Aijia Ouyang   +7 more
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Progress of Multimodal Imaging: Bridging Orthodontics to Comprehensive Oral Multidisciplinary Care

International Dental Journal
Introduction and aims: For decades, orthodontic diagnosis and treatment have primarily depended on physical models, imaging techniques, and three-dimensional (3D) imaging technologies.
Yuqian Feng, Yifan Jia, Linhe Lv, Xiaoxi Wei, Min Hu   +4 more
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Attention-Based Multimodal Fusion for Salience-Aware Blended Emotion Recognition

Multimodal Technologies and Interaction
Blended emotion recognition introduces the challenge of identifying not only which emotions are present in an expressive display but also their relative salience.
José Salas-Cáceres, Modesto Castrillón-Santana, Oliverio J. Santana, Daniel Hernández-Sosa, Javier Lorenzo-Navarro   +4 more
doaj   +1 more source

FDGSNet: A Multimodal Gated Segmentation Network for Remote Sensing Image Based on Frequency Decomposition

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Multiple modal data fusion can provide valuable and diverse information for remote sensing image segmentation. However, the existing fusion methods often lead to feature loss during the fusion of various modal data, and the complementarity among ...
Jian Cui, Jiahang Liu, Yue Ni, Jinjin Wang, Manchun Li   +4 more
doaj   +1 more source

Automatic fused multimodal deep learning for plant identification

Frontiers in Plant Science
IntroductionPlant classification is vital for ecological conservation and agricultural productivity, enhancing our understanding of plant growth dynamics and aiding species preservation. The advent of deep learning (DL) techniques has revolutionized this
Alfreds Lapkovskis, Natalia Nefedova, Ali Beikmohammadi   +2 more
doaj   +1 more source

Text–image multimodal fusion model for enhanced fake news detection

Science Progress
In the era of rapid internet expansion and technological progress, discerning real from fake news poses a growing challenge, exposing users to potential misinformation.
Szu-Yin Lin, Yen-Chiu Chen, Yu-Han Chang, Shih-Hsin Lo, Kuo-Ming Chao   +4 more
doaj   +1 more source

Multimodal Fusion

The following vacant PhD position will be active in the first grand challenge of this impulse program, called Data Science: Hybrid, Automated, Trusted, and Actionable and will include the following topics: - AI-based Model fusion: At a system level, multiple predictive models are often integrated to support a decision.
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Multimodal Fusion Network for Multimodal Sentiment Analysis

Recent advances in pretrained language models have reshaped multimodal learning, yet this progress often comes with increased computational demands. In this paper, we introduce the \textbf{Enhanced Multimodal Fusion Network (EMFN)}, a novel architecture that integrates textual, acoustic, and visual signals for sentiment analysis.
Blythe Ellison, Emily Marwood, Huxley Sinclair   +2 more
openaire   +1 more source