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High-level Understanding of Visual Content in Learning Materials through Graph Neural Networks

Monica Laura Zündorf, Rainer Stiefelhagen
Jürgen Beyerer for agreeing to be a reviewer of this thesis and for giving valuable feedback.  ...  I am very grateful for the wide range of opportunities that I was given at the lab: conducting research, writing proposals, designing lectures, supervising students, coordinating the practical course,  ...  In this chapter, we introduce a novel architecture for high-level reasoning of several image types which represents the relational structure of the visual data through a graph.  ... 
doi:10.5445/ir/1000143206 fatcat:x4ubsbiusndtfjwj6ocqcy3oei

Piano Teaching Knowledge Graph Construction Based on Cross-Media Data Analysis and Semantic Network

Han Li, Gengxin Sun
2022 Computational Intelligence and Neuroscience  
The great overall performance of piano instructing understanding graph mannequin primarily based on semantic network is validated through experiments.  ...  technology in piano teaching and constructs a multimodal knowledge Atlas of piano teaching based on deep neural network, so as to make piano teaching more intelligent and improve students' learning efficiency  ...  nodes, and edges in the expertise graph, units the precursor prerequisites for the understanding points, assigns a state, and recommends the subsequent gaining knowledge of course and content material  ... 
doi:10.1155/2022/5499593 pmid:35755737 pmcid:PMC9225848 fatcat:ft5itsab3zgmdcjpj3dsidu7iy

Understanding in Artificial Intelligence [article]

Stefan Maetschke and David Martinez Iraola and Pieter Barnard and Elaheh ShafieiBavani and Peter Zhong and Ying Xu and Antonio Jimeno Yepes
2021 arXiv   pre-print
Current Artificial Intelligence (AI) methods, most based on deep learning, have facilitated progress in several fields, including computer vision and natural language understanding.  ...  We show how progress has been made in benchmark development to measure understanding capabilities of AI methods and we review as well how current methods develop understanding capabilities.  ...  Arguably, for many of these benchmarks the level of understanding is limited and neural network based function approximators are able to provide a high level of accuracy.  ... 
arXiv:2101.06573v1 fatcat:nlp6h5toh5f6lpwjsafn6gulbq

A survey of visual analytics techniques for machine learning

Jun Yuan, Changjian Chen, Weikai Yang, Mengchen Liu, Jiazhi Xia, Shixia Liu
2020 Computational Visual Media  
AbstractVisual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization.  ...  To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics, we systematically review 259 papers published in the last ten years together with  ...  , 61672308, and 61936002), TC190A4DA/3, the Institute Guo Qiang, Tsinghua University, and in part by Tsinghua-Kuaishou Institute of Future Media Data.  ... 
doi:10.1007/s41095-020-0191-7 fatcat:ibrmost24rgtnctztrmcdvz6dq

Deep learning approaches to pattern extraction and recognition in paintings and drawings: an overview

Giovanna Castellano, Gennaro Vessio
2021 Neural computing & applications (Print)  
AbstractThis paper provides an overview of some of the most relevant deep learning approaches to pattern extraction and recognition in visual arts, particularly painting and drawing.  ...  Recent advances in deep learning and computer vision, coupled with the growing availability of large digitized visual art collections, have opened new opportunities for computer science researchers to  ...  Acknowledgements Gennaro Vessio acknowledges the financial support of the Italian Ministry of University and Research through the PON AIM 1852414 project.  ... 
doi:10.1007/s00521-021-05893-z fatcat:elqzw3hzbzgodotie6ndih537u

Neural Network Design using a Virtual Reality Platform

Luigi Bibbò, Francesco Carlo Morabito
2022 Global Journal of Computer Science and Technology  
Data visualization in deep learning is a fundamental element for which it can benefit from the advantages offered by the visualization of the VR for the development of the models.  ...  In this study, we want to demonstrate the usefulness of this approach in collecting data within virtual reality to train and optimize a convolutional neural network used to recognize human activities (  ...  Acknowledgments This work was developed as an integral component of the project: "An integrated System for indoor people localization, tracking, and monitoring" supported by the Italian MIUR under GRANT  ... 
doi:10.34257/gjcstdvol22is1pg45 fatcat:hstjms4hwjc37jpk3jjzdwrz44

Development of Web Based Courseware for Artificial Neural Networks

Mehmet BILEN, Ali Hakan ISIK, Tuncay YIGIT
. • Artificial neural network procedures can be performed and results can be traced in the courseware. • Courseware has the ability to change initial parameters and observe outputs in real time. • Students  ...  can understand better the working mechanism of ANN with the help of the courseware.  ...  In the literature, the aim is to provide a better understanding of biological neural networks, but also in studies that allow the production and use of artificial neural networks [17, 18] .  ... 
doi:10.35378/gujs.473450 fatcat:wdgajpmjsfc7lprm3okkhqdwfy

Semantic Relation Model and Dataset for Remote Sensing Scene Understanding

Peng Li, Dezheng Zhang, Aziguli Wulamu, Xin Liu, Peng Chen
2021 ISPRS International Journal of Geo-Information  
In addition, RSSGD effectively bridges the huge semantic gap between low-level perception and high-level cognition of remote sensing images.  ...  Therefore, the recognition of semantic relations is conducive to strengthen the understanding of remote sensing scenes. In this paper, we propose a novel multi-scale semantic fusion network (MSFN).  ...  A context-dependent diffusion network [47] learned semantic knowledge through a word graph and obtained spatial representation by extracting the low-level features, then these two types of global context  ... 
doi:10.3390/ijgi10070488 fatcat:jrdojgzgfzektfk3t2a564x5ay

Data-Mining Research in Education [article]

Jiechao Cheng
2017 arXiv   pre-print
Applying data mining in education also known as educational data mining (EDM), which enables to better understand how students learn and identify how improve educational outcomes.  ...  Present paper is designed to justify the capabilities of data mining approaches in the filed of education. The latest trends on EDM research are introduced in this review.  ...  usually has meaningful hierarchy at multiple levels, in order to discover new insights of how people learn in the context of these settings [11] .  ... 
arXiv:1703.10117v2 fatcat:4aujugkxcnbhlivfg5zt7of56e

Knowledge Graph Embedding-Based Domain Adaptation for Musical Instrument Recognition

Victoria Eyharabide, Imad Eddine Ibrahim Bekkouch, Nicolae Dragoș Constantin
2021 Computers  
We combined knowledge graph embeddings with visual embeddings from the images and trained a neural network with the combined embeddings as anchors using an extension of Fisher's linear discriminant.  ...  Convolutional neural networks raised the bar for machine learning and artificial intelligence applications, mainly due to the abundance of data and computations.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/computers10080094 fatcat:jflkr5uwqvazdop2pfhsqj73lq

Data fusion methods in multimodal human computer dialog

Ming-Hao YANG, Jian-Hua TAO
2019 Virtual Reality & Intelligent Hardware  
Nowadays, in spite of high performance of users' single channel behavior computing, it is still great challenge to understand users' intention accurately from their multimodal behaviors.  ...  Intelligence methods of multi-modal information fusion in human-computer interaction SCIENTIA SINICA Informationis 48, 433 (2018); Rapid and High-quality 3D Fusion of Heterogeneous CT and MRI Data for  ...  matching patterns at different levels of abstraction; (2) helps to generate the continuous latent variable representing the high-level semantic content of the response and the response word by word conditioned  ... 
doi:10.3724/sp.j.2096-5796.2018.0010 dblp:journals/vrih/YangT19 fatcat:jltufn3o6fd4pjzv4pnqd6wmvy

Structure motif-centric learning framework for inorganic crystalline systems

Huta R Banjade, Sandro Hauri, Shanshan Zhang, Francesco Ricci, Weiyi Gong, Geoffroy Hautier, Slobodan Vucetic, Qimin Yan
2021 Science Advances  
The work illustrates the route toward fundamental design of graph neural network learning architecture for complex materials by incorporating beyond-atom physical principles.  ...  graph network (AMDNet), which is more accurate in predicting the electronic structures of metal oxides such as bandgaps.  ...  Figure 1 shows the high-level representation of the workflow used in the unsupervised learning algorithm.  ... 
doi:10.1126/sciadv.abf1754 pmid:33883136 pmcid:PMC8059928 fatcat:x3h772idqvhmjmo4xfaq6rcr44

Deep understanding of 3-D multimedia information retrieval on social media: implications and challenges

Ritika Wason, Vishal Jain, Gagandeep Singh Narula, Anupam Balyan
2019 Iran Journal of Computer Science  
This has imposed high demands on multimedia information retrieval (MIR) techniques. This manuscript illustrates the MIR concept in terms of its application to social media.  ...  The big explosion of multimedia data on the web has enabled social networks to gauge user likes, dislikes, and needs.  ...  of these systems does not ensure that they are trained to perfectly resolve high-level problems 2 A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme [42  ... 
doi:10.1007/s42044-019-00030-5 fatcat:e7kgskeqxbaznbjh3hrmhw3nke

MOMA: Multi-Object Multi-Actor Activity Parsing

Zelun Luo, Wanze Xie, Siddharth Kapoor, Yiyun Liang, Michael Cooper, Juan Carlos Niebles, Ehsan Adeli, Fei-Fei Li
2021 Neural Information Processing Systems  
In recent years, graph neural networks [85, 56] have been found to be a promising tool for both generating and learning from visual compositions.  ...  Graph neural networks (GNNs) were first introduced in [20, 64] , and has been recently adopted for video understanding.  ...  Temporal action detection with structured segment networks. In Proceedings of the IEEE International Conference on Computer Vision, pages 2914-2923, 2017.  ... 
dblp:conf/nips/LuoXKLCNAL21 fatcat:7sana34l75b4zdlgdunqjufk74

Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph

AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, Erik Cambria, Louis-Philippe Morency
2018 Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
Intrinsically human communication is multimodal (heterogeneous), temporal and asynchronous; it consists of the language (words), visual (expressions), and acoustic (paralinguistic) modalities all in the  ...  Analyzing human multimodal language is an emerging area of research in NLP.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of National Science Foundation or Oculus VR, and  ... 
doi:10.18653/v1/p18-1208 dblp:conf/acl/MorencyCPLZ18 fatcat:g25wcs5dcbglnmbksasn5qlis4
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