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1202
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Feature extraction
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Bridges, Gold, Knowledge based systems, Medical services,
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1712
Analytical models, Buildings, Computer architecture, Context,
Machine learning, Sentiment analysis, Social network services,
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1709
Sentiment
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1709
Sentiment
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Aspect-Based Extraction and Analysis of Affective Knowledge from
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IEEE_Int_Sys(32), No. 3, May 2017, pp. 80-88.
IEEE DOI
1706
Automobiles, Companies, Data mining, Knowledge acquisition, Media,
Sentiment analysis, Social network services,
affective knowledge extraction, artificial intelligence,
aspect-based sentiment analysis, linked data, opinion targets,
social, media
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Pappas, N.[Nikolaos],
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1704
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IEEE DOI
1808
Ontologies, Sentiment analysis, Semantics, Task analysis,
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Elsevier DOI
1812
Using images to express opinions and share experiences.
Image sentiment, Label distribution learning,
Structured sparsity, Low-rank
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1809
image representation, learning (artificial intelligence),
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1901
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1901
Affective image classification, Discrete emotion space,
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1903
Painting, Face, Pose estimation, Training, Databases, Electronic mail,
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1905
computer vision, convolutional neural nets, image representation,
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1906
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1908
Machine learning, Multi-cue fusion, Aesthetic tendency, Flickr, Graph mining
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IEEE DOI
1911
Task analysis, Probabilistic logic, Computational modeling,
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unified probabilistic formulation
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He, L.,
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IEEE DOI
1911
Feature extraction, Logic gates, Visualization,
Convolutional neural networks, Task analysis, Deep learning,
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Convolutional neural networks, Fine art,
High-level image features, Wölfflin
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IEEE DOI
2002
Image aesthetics assessment,
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Siamese network
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IEEE DOI
2003
Databases, Predictive models, Visualization, Analytical models, Face,
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She, D.,
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IEEE DOI
2005
Visualization, Proposals, Task analysis, Feature extraction,
Sentiment analysis, Training, Convolutional neural networks,
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Yang, J.,
She, D.,
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Yang, M.,
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CVPR18(7584-7592)
IEEE DOI
1812
Visualization, Proposals, Feature extraction, Sentiment analysis,
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Huang, S.,
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2007
Sparse representation, target detection, paint loss, kernel,
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Kim, W.,
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AffCom(11), No. 3, July 2020, pp. 493-506.
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2008
Quality assessment, Photography, Databases, Visualization, Semantics,
Computational modeling, Image processing, Photograph, subjectivity,
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Zhao, L.[Lin],
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Gao, F.[Fei],
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Elsevier DOI
2009
Photo quality assessment, Image quality assessment,
Deep learning, Aesthetic, Representation learning
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Zhang, C.[Chao],
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2009
Video aesthetic assessment, Structure correlation, SVM
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Kuang, Q.,
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Zhao, Q.,
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Deep Multimodality Learning for UAV Video Aesthetic Quality
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2009
Quality assessment, Cameras, Feature extraction, Photography, Drones,
Streaming media, Aesthetic quality assessment,
deep multimodality learning
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ICIVC20(317-321)
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2009
Product design, Genetic algorithms, Sociology, Statistics,
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Pilli, S.,
Patwardhan, M.,
Pedanekar, N.,
Karande, S.,
Predicting Sentiments in Image Advertisements using Semantic
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EmotioNet20(1640-1648)
IEEE DOI
2008
Semantics, Convolution, Measurement, Feature extraction,
Mathematical model, Neural networks, Visualization
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Polanía, L.F.,
Flores, M.,
Nokleby, M.,
Li, Y.,
Learning Furniture Compatibility with Graph Neural Networks,
WiCV20(1505-1513)
IEEE DOI
2008
Feature extraction, Computational modeling, Data models,
Neural networks, Computer vision, Logic gates, Task analysis
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Li, D.,
Zhang, J.,
Huang, K.,
Yang, M.,
Composing Good Shots by Exploiting Mutual Relations,
CVPR20(4212-4221)
IEEE DOI
2008
Feature extraction, Logic gates, Predictive models, Convolution,
Cognition, Task analysis, Correlation
BibRef
Chen, Q.,
Zhang, W.,
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Lei, P.,
Xu, Y.,
Zheng, Y.,
Fan, J.,
Adaptive Fractional Dilated Convolution Network for Image Aesthetics
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CVPR20(14102-14111)
IEEE DOI
2008
Kernel, Convolution, Training, Machine learning, Interpolation,
Task analysis, Libraries
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Liu, D.,
Puri, R.,
Kamath, N.,
Bhattacharya, S.,
Composition-Aware Image Aesthetics Assessment,
WACV20(3558-3567)
IEEE DOI
2006
Visualization, Convolution, Computational modeling, Cognition,
Image edge detection, Task analysis, Integrated circuits
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Ghosal, K.,
Rana, A.,
Smolic, A.,
Aesthetic Image Captioning From Weakly-Labelled Photographs,
CroMoL19(4550-4560)
IEEE DOI
2004
convolutional neural nets, feature extraction, image annotation,
image filtering, Internet, photography, probability,
Noisy Data
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Kastner, M.A.[Marc A.],
Ide, I.[Ichiro],
Kawanishi, Y.[Yasutomo],
Hirayama, T.[Takatsugu],
Deguchi, D.[Daisuke],
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Browsing Visual Sentiment Datasets Using Psycholinguistic Groundings,
MMMod20(II:697-702).
Springer DOI
2003
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Li, Z.Q.[Zheng-Qing],
Zha, Z.J.[Zheng-Jun],
Cao, Y.[Yang],
Deep Palette-based Color Decomposition for Image Recoloring with
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Springer DOI
2003
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Efros, A.A.[Alexei A.],
Aubry, M.[Mathieu],
Discovering Visual Patterns in Art Collections With
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IEEE DOI
2002
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Huang, C.[Chong],
Lin, C.E.[Chuan-En],
Yang, Z.Y.[Zhen-Yu],
Kong, Y.[Yan],
Chen, P.[Peng],
Yang, X.[Xin],
Cheng, K.T.[Kwang-Ting],
Learning to Film From Professional Human Motion Videos,
CVPR19(4239-4248).
IEEE DOI
2002
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Hosu, V.[Vlad],
Goldlucke, B.[Bastian],
Saupe, D.[Dietmar],
Effective Aesthetics Prediction With Multi-Level Spatially Pooled
Features,
CVPR19(9367-9375).
IEEE DOI
2002
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Ye, J.[Jin],
Peng, X.J.[Xiao-Jiang],
Qiao, Y.[Yu],
Xing, H.[Hao],
Li, J.L.[Jun-Li],
Ji, R.R.[Rong-Rong],
Visual-Textual Sentiment Analysis in Product Reviews,
ICIP19(869-873)
IEEE DOI
1910
sentiment analysis, product reviews, tucker decomposition, DTF
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Wang, W.N.[Wei-Ning],
Su, J.[Junjie],
Li, L.[Lemin],
Xu, X.M.[Xiang-Min],
Luo, J.[Jiebo],
Meta-Learning Perspective for Personalized Image Aesthetics
Assessment,
ICIP19(1875-1879)
IEEE DOI
1910
Image Aesthetics, Personalized Preference, Meta-Learning,
Meta regularization, Deep Learning
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Zhang, W.,
Zhai, G.,
Yang, X.,
Yan, J.,
Hierarchical Features Fusion for Image Aesthetics Assessment,
ICIP19(3771-3775)
IEEE DOI
1910
Image Aesthetics Assessment, Low-rank Bilinear Pooling, Hierarchical Features
BibRef
Yu, J.,
Cui, C.,
Geng, L.,
Ma, Y.,
Yin, Y.,
Towards Unified Aesthetics and Emotion Prediction in Images,
ICIP19(2526-2530)
IEEE DOI
1910
Aesthetics assessment, emotion recognition, multi-task learning
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Wang, W.,
Deng, R.,
Modeling Human Perception for Image Aesthetic Assessment,
ICIP19(1029-1033)
IEEE DOI
1910
Image Aesthetic Assessment, Deep Neural Network,
Attractive Region, Adaptive Aggregation
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Bilen, H.[Hakan],
Personalised Aesthetics with Residual Adapters,
IbPRIA19(I:508-520).
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Pierdicca, R.[Roberto],
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Visual and Textual Sentiment Analysis of Daily News Social Media Images
by Deep Learning,
CIAP19(I:477-487).
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Cornia, M.[Marcella],
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Artpedia: A New Visual-Semantic Dataset with Visual and Contextual
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CIAP19(II:729-740).
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1909
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Images of Image Machines. Visual Interpretability in Computer Vision
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CVAA18(II:710-715).
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1905
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Garcia, N.[Noa],
Vogiatzis, G.[George],
How to Read Paintings: Semantic Art Understanding with Multi-modal
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Ladjal, S.[Said],
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Weakly Supervised Object Detection in Artworks,
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1905
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Cianci, M.G.,
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Information Modeling and Landscape: Intervention Methodology For
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3DARCH19(269-276).
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Aesthetics of landscapes.
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Ma, N.,
Volkov, A.,
Livshits, A.,
Pietrusinski, P.,
Hu, H.,
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An Universal Image Attractiveness Ranking Framework,
WACV19(657-665)
IEEE DOI
1904
image classification, image retrieval, indexing,
learning (artificial intelligence), neural nets, search engines,
Indexes
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Saito, J.[Junki],
Nakamura, S.[Satoshi],
Fontender: Interactive Japanese Text Design with Dynamic Font Fusion
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MMMod19(II:554-559).
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1901
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Apostolidis, K.[Konstantinos],
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Image Aesthetics Assessment Using Fully Convolutional Neural Networks,
MMMod19(I:361-373).
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1901
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Wei, Z.,
Zhang, J.,
Shen, X.,
Lin, Z.,
Mech, R.,
Hoai, M.,
Samaras, D.,
Good View Hunting: Learning Photo Composition from Dense View Pairs,
CVPR18(5437-5446)
IEEE DOI
1812
Proposals, Training, Agriculture, Task analysis, Knowledge transfer,
Protocols, Virtual private networks
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Liu, W.,
Fu, X.,
Introduce More Characteristics of Samples into Cross-domain Sentiment
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ICPR18(25-30)
IEEE DOI
1812
Training, Adaptation models, Neural networks, Task analysis,
Training data, Mathematical model, Data models
BibRef
Fan, S.,
Shen, Z.,
Jiang, M.,
Koenig, B.L.,
Xu, J.,
Kankanhalli, M.S.,
Zhao, Q.,
Emotional Attention: A Study of Image Sentiment and Visual Attention,
CVPR18(7521-7531)
IEEE DOI
1812
Semantics, Visualization, Computational modeling, Observers,
Neural networks, Benchmark testing, Image annotation
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Paolanti, M.[Marina],
Kaiser, C.[Carolin],
Schallner, R.[René],
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Visual and Textual Sentiment Analysis of Brand-Related Social Media
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CIAP17(I:402-413).
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1711
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Ullah, M.A.,
Islam, M.M.,
Azman, N.B.,
Zaki, Z.M.,
An overview of Multimodal Sentiment Analysis research:
Opportunities and Difficulties,
IVPR17(1-6)
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1704
Face
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Nemati, S.,
Naghsh-Nilchi, A.R.,
Exploiting evidential theory in the fusion of textual, audio, and
visual modalities for affective music video retrieval,
IPRIA17(222-228)
IEEE DOI
1712
emotion recognition, image fusion, inference mechanisms,
sentiment analysis, social networking (online),
Lexicon-based sentiment analysis
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Wu, L.,
Liu, S.,
Jian, M.,
Luo, J.,
Zhang, X.,
Qi, M.,
Reducing noisy labels in weakly labeled data for visual sentiment
analysis,
ICIP17(1322-1326)
IEEE DOI
1803
Indexes, Visual sentiment analysis, deep learning,
mislabeled images, sentiment conflict
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Chen, X.,
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Visual and textual sentiment analysis using deep fusion convolutional
neural networks,
ICIP17(1557-1561)
IEEE DOI
1803
Convolutional neural networks, Feature extraction, Semantics,
Sentiment analysis, Social network services, Training,
visual sentiment
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Zheng, H.,
Chen, T.,
You, Q.,
Luo, J.,
When saliency meets sentiment: Understanding how image content
invokes emotion and sentiment,
ICIP17(630-634)
IEEE DOI
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Analytical models, Computational modeling, Correlation, Proposals,
Saliency detection, Sentiment analysis, Visualization, saliency,
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Sentiment Analysis on Multi-View Social Data,
MMMod16(II: 15-27).
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Shin, A.[Andrew],
Ushiku, Y.[Yoshitaka],
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Image Captioning with Sentiment Terms via Weakly-Supervised Sentiment
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BMVC16(xx-yy).
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1805
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Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Rendering Specific Surfaces, Applied Rendering .