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Relevant bibliographies by topics / Deep Learning Fusion

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Academic literature on the topic 'Deep Learning Fusion'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Deep Learning Fusion"

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Shetty D S, Radhika. "Multi-Modal Fusion Techniques in Deep Learning." International Journal of Science and Research (IJSR) 12, no. 9 (2023): 526–32. http://dx.doi.org/10.21275/sr23905100554.

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P, Jayapal. "Efficient Human-Machine Interface through Deep Learning Fusion." International Journal of Science and Research (IJSR) 13, no. 1 (2024): 680–86. http://dx.doi.org/10.21275/sr24109210845.

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Jianwei Chen, Jianwei Chen, Quan Du Jianwei Chen, and Ling-Ju Hung Quan Du. "A Fusion Algorithm Based on Deep Learning for Panoramic Image." 電腦學刊 35, no. 6 (2024): 097–107. https://doi.org/10.53106/199115992024123506008.

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<p>Traditional image fusion algorithms often struggle with slow processing speeds and suboptimal results, particularly when handling non-planar images. In this paper, we present a novel deep learning-based approach for panoramic image fusion. We begin by detailing our dataset construction and preprocessing techniques. To enhance the model&rsquo;s capability with non-planar images, we apply the Thin Plate Spline (TPS) deformation algorithm, allowing effective panoramic fusion across complex image structures. The model architecture is based on a convolutional neural network (CNN) frame

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Sun, Changqi, Cong Zhang, and Naixue Xiong. "Infrared and Visible Image Fusion Techniques Based on Deep Learning: A Review." Electronics 9, no. 12 (2020): 2162. http://dx.doi.org/10.3390/electronics9122162.

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Infrared and visible image fusion technologies make full use of different image features obtained by different sensors, retain complementary information of the source images during the fusion process, and use redundant information to improve the credibility of the fusion image. In recent years, many researchers have used deep learning methods (DL) to explore the field of image fusion and found that applying DL has improved the time-consuming efficiency of the model and the fusion effect. However, DL includes many branches, and there is currently no detailed investigation of deep learning metho

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Zhong, Hongye, and Jitian Xiao. "Enhancing Health Risk Prediction with Deep Learning on Big Data and Revised Fusion Node Paradigm." Scientific Programming 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/1901876.

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With recent advances in health systems, the amount of health data is expanding rapidly in various formats. This data originates from many new sources including digital records, mobile devices, and wearable health devices. Big health data offers more opportunities for health data analysis and enhancement of health services via innovative approaches. The objective of this research is to develop a framework to enhance health prediction with the revised fusion node and deep learning paradigms. Fusion node is an information fusion model for constructing prediction systems. Deep learning involves th

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Janani, T., and A. Ramanan. "Feature Fusion for Efficient Object Classification Using Deep and Shallow Learning." International Journal of Machine Learning and Computing 7, no. 5 (2017): 123–27. http://dx.doi.org/10.18178/ijmlc.2017.7.5.633.

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Tu, Wenxuan, Sihang Zhou, Xinwang Liu, et al. "Deep Fusion Clustering Network." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (2021): 9978–87. http://dx.doi.org/10.1609/aaai.v35i11.17198.

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Deep clustering is a fundamental yet challenging task for data analysis. Recently we witness a strong tendency of combining autoencoder and graph neural networks to exploit structure information for clustering performance enhancement. However, we observe that existing literature 1) lacks a dynamic fusion mechanism to selectively integrate and refine the information of graph structure and node attributes for consensus representation learning; 2) fails to extract information from both sides for robust target distribution (i.e., “groundtruth” soft labels) generation. To tackle the above issues, w

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Vielzeuf, Valentin, Alexis Lechervy, Stephane Pateux, and Frederic Jurie. "Multilevel Sensor Fusion With Deep Learning." IEEE Sensors Letters 3, no. 1 (2019): 1–4. http://dx.doi.org/10.1109/lsens.2018.2878908.

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Shi, Haobin, Meng Xu, Kao-Shing Hwang, and Bo-Yin Cai. "Behavior fusion for deep reinforcement learning." ISA Transactions 98 (March 2020): 434–44. http://dx.doi.org/10.1016/j.isatra.2019.08.054.

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Gao, Jing, Peng Li, Zhikui Chen, and Jianing Zhang. "A Survey on Deep Learning for Multimodal Data Fusion." Neural Computation 32, no. 5 (2020): 829–64. http://dx.doi.org/10.1162/neco_a_01273.

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With the wide deployments of heterogeneous networks, huge amounts of data with characteristics of high volume, high variety, high velocity, and high veracity are generated. These data, referred to multimodal big data, contain abundant intermodality and cross-modality information and pose vast challenges on traditional data fusion methods. In this review, we present some pioneering deep learning models to fuse these multimodal big data. With the increasing exploration of the multimodal big data, there are still some challenges to be addressed. Thus, this review presents a survey on deep learnin

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Dissertations / Theses on the topic "Deep Learning Fusion"

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Howard, Shaun Michael. "Deep Learning for Sensor Fusion." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1495751146601099.

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Nguyen, Tien Dung. "Multimodal emotion recognition using deep learning techniques." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/180753/1/Tien%20Dung_Nguyen_Thesis.pdf.

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This thesis investigates the use of deep learning techniques to address the problem of machine understanding of human affective behaviour and improve the accuracy of both unimodal and multimodal human emotion recognition. The objective was to explore how best to configure deep learning networks to capture individually and jointly, the key features contributing to human emotions from three modalities (speech, face, and bodily movements) to accurately classify the expressed human emotion. The outcome of the research should be useful for several applications including the design of social robots.

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Andrade, Valente da Silva Michelle. "SLAM and data fusion for autonomous vehicles : from classical approaches to deep learning methods." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEM079.

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L'arrivée des voitures autonomes va provoquer une transformation très importante de la mobilité urbaine telle que nous la connaissons, avec un impact significatif sur notre vie quotidienne. En effet, elles proposent un nouveau système de déplacement plus efficace, plus facilement accessible et avec une meilleure sécurité routière. Pour atteindre cet objectif, les véhicules autonomes doivent effectuer en toute sécurité et de manière autonome trois tâches principales: la perception, la planification et le contrôle. La perception est une tâche particulièrement difficile en milieu urbain, car elle

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Bodén, Johan. "A Comparative Study of Reinforcement-based and Semi-classical Learning in Sensor Fusion." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-84784.

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Reinforcement learning has proven itself very useful in certain areas, such as games. However, the approach has been seen as quite limited. Reinforcement-based learning has for instance not been commonly used for classification tasks as it is receiving feedback on how well it did for an action performed on a specific input. This slows the performance convergence rate as compared to other classification approaches which has the input and the corresponding output to train on. Nevertheless, this thesis aims to investigate whether reinforcement-based learning could successfully be employed on a cl

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Abd, Gaus Yona Falinie. "Artificial intelligence system for continuous affect estimation from naturalistic human expressions." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/16348.

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The analysis and automatic affect estimation system from human expression has been acknowledged as an active research topic in computer vision community. Most reported affect recognition systems, however, only consider subjects performing well-defined acted expression, in a very controlled condition, so they are not robust enough for real-life recognition tasks with subject variation, acoustic surrounding and illumination change. In this thesis, an artificial intelligence system is proposed to continuously (represented along a continuum e.g., from -1 to +1) estimate affect behaviour in terms o

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Baier, Stephan [Verfasser], and Volker [Akademischer Betreuer] Tresp. "Learning representations for supervised information fusion using tensor decompositions and deep learning methods / Stephan Baier ; Betreuer: Volker Tresp." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2019. http://d-nb.info/1185979220/34.

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TOOSI, AMIRHOSEIN. "Feature Fusion for Fingerprint Liveness Detection." Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2711594.

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For decades, fingerprints have been the most widely used biometric trait in identity recognition systems, thanks to their natural uniqueness, even in rare cases such as identical twins. Recently, we witnessed a growth in the use of fingerprint-based recognition systems in a large variety of devices and applications. This, as a consequence, increased the benefits for offenders capable of attacking these systems. One of the main issues with the current fingerprint authentication systems is that, even though they are quite accurate in terms of identity verification, they can be easily spoo

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Sha, Mingzhi. "A Novel Semantic Feature Fusion-based Pedestrian Detection System to Support Autonomous Vehicles." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42213.

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Intelligent transportation systems (ITS) have become a popular method to enhance the safety and efficiency of transportation. Pedestrians, as an essential participant of ITS, are very vulnerable in a traffic collision, compared with the passengers inside the vehicle. In order to protect the safety of all traffic participants and enhance transportation efficiency, the novel autonomous vehicles are required to detect pedestrians accurately and timely. In the area of pedestrian detection, deep learning-based pedestrian detection methods have gained significant development since the appearance

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Praboda, Chathurangani Rajapaksha Rajapaksha Waththe Vidanelage. "Clickbait detection using multimodel fusion and transfer learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAS025.

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Presque tous les internautes sont susceptibles d'être victimes de clickbait, supposant à tort qu’il s’agit d’informations légitimes. Un type important de clickbait se présente sous la forme de spam et de publicités qui sont utilisés pour rediriger les utilisateurs vers des sites web. Un autre type de "clickbait" est conçu pour faire la une des journaux et rediriger les lecteurs vers leurs sites en ligne, mais ces nouvelles sensationnelles peuvent être trompeuses. Il est difficile de prédire le degré de click-baity d'une nouvelle donnée car les clickbait sont des messages très courts et écrits

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Stone, David L. "The Application of Index Based, Region Segmentation, and Deep Learning Approaches to Sensor Fusion for Vegetation Detection." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5708.

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This thesis investigates the application of index based, region segmentation, and deep learning methods to the sensor fusion of omnidirectional (O-D) Infrared (IR) sensors, Kinnect sensors, and O-D vision sensors to increase the level of intelligent perception for unmanned robotic platforms. The goals of this work is first to provide a more robust calibration approach and improve the calibration of low resolution and noisy IR O-D cameras. Then our goal was to explore the best approach to sensor fusion for vegetation detection. We looked at index based, region segmentation, and deep learning me

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Books on the topic "Deep Learning Fusion"

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Lee, Sukhan, Hanseok Ko, and Songhwai Oh, eds. Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90509-9.

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Liu, Xueqing. Fusing simultaneously acquired EEG-fMRI using deep learning. [publisher not identified], 2022.

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Kehtarnavaz, Nasser, and Arian Azarang. Image Fusion in Remote Sensing: Conventional and Deep Learning Approaches. Morgan & Claypool Publishers, 2021.

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Kehtarnavaz, Nasser, and Arian Azarang. Image Fusion in Remote Sensing: Conventional and Deep Learning Approaches. Morgan & Claypool Publishers, 2021.

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Kehtarnavaz, Nasser, and Arian Azarang. Image Fusion in Remote Sensing: Conventional and Deep Learning Approaches. Springer International Publishing AG, 2021.

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Kehtarnavaz, Nasser, and Arian Azarang. Image Fusion in Remote Sensing: Conventional and Deep Learning Approaches. Morgan & Claypool Publishers, 2021.

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Pour, Amin Beiranvand, Omeid Rahmani, and Mohammad Parsa, eds. Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques. MDPI, 2023. http://dx.doi.org/10.3390/books978-3-0365-6794-5.

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Lee, Sukhan, Hanseok Ko, and Songhwai Oh. Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System: An Edition of the Selected Papers from the 2017 ... Notes in Electrical Engineering ). Springer, 2018.

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Lee, Sukhan, Hanseok Ko, and Songhwai Oh. Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System: An Edition of the Selected Papers from the 2017 ... Notes in Electrical Engineering ). Springer, 2018.

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Wishart, Jeffrey, Yan Chen, Steven Como, Narayanan Kidambi, Duo Lu, and Yezhou Yang. Fundamentals of Connected and Automated Vehicles. SAE International, 2022. http://dx.doi.org/10.4271/9780768099829.

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The automotive industry is transforming to a greater degree that has occurred since Henry Ford introduced mass production of the automobile with the Model T in 1913. Advances in computing, data processing, and artificial intelligence (deep learning in particular) are driving the development of new levels of automation that will impact all aspects of our lives including our vehicles. What are Connected and Automated Vehicles (CAVs)? What are the underlying technologies that need to mature and converge for them to be widely deployed? Fundamentals of Connected and Automated Vehicles is written to

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Book chapters on the topic "Deep Learning Fusion"

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Li, Jinxing, Bob Zhang, and David Zhang. "Information Fusion Based on Deep Learning." In Information Fusion. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8976-5_7.

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Xiao, Gang, Durga Prasad Bavirisetti, Gang Liu, and Xingchen Zhang. "Image Fusion Based on Machine Learning and Deep Learning." In Image Fusion. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4867-3_7.

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Sheikh, Ashif, Jitesh Pradhan, Arpit Dhuriya, and Arup Kumar Pal. "Medical Image Fusion Using Deep Learning." In Deep Learning for Biomedical Data Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71676-9_6.

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Krishnan, Palani Thanaraj, and Vijayarajan Rajangam. "Image Fusion Using Deep Learning Methods." In Advanced Image Fusion Techniques for Medical Imaging. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-7602-6_3.

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Krishnan, Palani Thanaraj, and Vijayarajan Rajangam. "Fusion Strategies for Deep Learning Applications." In Advanced Image Fusion Techniques for Medical Imaging. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-7602-6_4.

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Raddekar, Ashwini, Akash Athani, Akshata Bhosle, Vaishnavi Divnale, and Diptee Chikmurge. "Emotion Detection Using Deep Fusion Model." In Proceedings in Adaptation, Learning and Optimization. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31164-2_40.

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Osório, Fernando, Bernard Amy, and Adelmo Cechin. "Hybrid Machine Learning Tools: INSS — A Neuro-Symbolic System for Constructive Machine Learning." In Deep Fusion of Computational and Symbolic Processing. Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1837-6_6.

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Wang, Kejun, Xuesen Hao, and Xianglei Xing. "Feature Level Information Fusion Based Deep Learning." In Lecture Notes in Electrical Engineering. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6445-6_55.

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Adi Narayana Reddy, K., Naveen Kumar Laskari, G. Shyam Chandra Prasad, and N. Sreekanth. "Fusion-Based Celebrity Profiling Using Deep Learning." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4863-3_10.

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Ekal, Saloni, Kunjal Wadke, Md Altamash, and Rupali Kute. "Face and Fingerprint Fusion Using Deep Learning." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6581-4_13.

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Conference papers on the topic "Deep Learning Fusion"

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Geletu, Mihreteab Negash, Jean-Philippe Lauffenburger, Thomas Josso-Laurain, Maxime Devanne, and Mengesha Mamo Wogari. "Evidential Deep Learning For Sensor Fusion." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706424.

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Kang, Jeong Min, Zoran Sjanic, and Gustaf Hendeby. "Visual-Inertial Odometry Using Optical Flow from Deep Learning." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706322.

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Zhang, Yuanhang, Zhidi Lin, Yiyong Sun, Feng Yin, and Carsten Fritsche. "Regularization-Based Efficient Continual Learning in Deep State-Space Models." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706515.

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Hao, Yuhang, Zengfu Wang, Jing Fu, and Quan Pan. "A Deep Reinforcement Learning-Based Whittle Index Policy for Multibeam Allocation." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706358.

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Xu, Guangwu. "Multi-Feature Fusion Network for Infrared and Visible Image Fusion." In 2025 6th International Conference on Computer Vision, Image and Deep Learning (CVIDL). IEEE, 2025. https://doi.org/10.1109/cvidl65390.2025.11085558.

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Kornfeld, Nils, Andreas Leich, and Michael Roth. "Kalman filtering aspects in camera and deep learning based tracking for traffic monitoring." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706402.

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Sghaier, Moslem Ouled, Melita Hadzagic, Jun Ye Yu, Sofia Shton, and Elisa Shahbazian. "Leveraging Generative Deep Learning Models for Enhanced Change Detection in Heterogeneous Remote Sensing Data." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706502.

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Sætran, Ole Halvard, and Sigmund Rolfsjord. "Enhancing Predicted Distributions for Constant Acceleration and Turn Rate Motion Models: A Deep Learning Approach." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706428.

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Gao, Dongying, Caiwei Guo, Wen Ji, et al. "Extended access control mechanism based on multiattribute fusion." In International Conference on Cloud Computing, Performance Computing, and Deep Learning, edited by Wanyang Dai and Xiangjie Kong. SPIE, 2024. http://dx.doi.org/10.1117/12.3050631.

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Shahsafi, Soroush, and Farnoosh Naderkhani. "Enhancing Stock Trading Performance with Deep Q-Learning by Addressing Noisy Data through Advanced Denoising Techniques." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706354.

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Reports on the topic "Deep Learning Fusion"

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Kulhandjian, Hovannes. Detecting Driver Drowsiness with Multi-Sensor Data Fusion Combined with Machine Learning. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2015.

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In this research work, we develop a drowsy driver detection system through the application of visual and radar sensors combined with machine learning. The system concept was derived from the desire to achieve a high level of driver safety through the prevention of potentially fatal accidents involving drowsy drivers. According to the National Highway Traffic Safety Administration, drowsy driving resulted in 50,000 injuries across 91,000 police-reported accidents, and a death toll of nearly 800 in 2017. The objective of this research work is to provide a working prototype of Advanced Driver Ass

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Kulhandjian, Hovannes. AI-based Pedestrian Detection and Avoidance at Night using an IR Camera, Radar, and a Video Camera. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2127.

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In 2019, the United States experienced more than 6,500 pedestrian fatalities involving motor vehicles which resulted in a 67% rise in nighttime pedestrian fatalities and only a 10% rise in daytime pedestrian fatalities. In an effort to reduce fatalities, this research developed a pedestrian detection and alert system through the application of a visual camera, infrared camera, and radar sensors combined with machine learning. The research team designed the system concept to achieve a high level of accuracy in pedestrian detection and avoidance during both the day and at night to avoid potentia

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