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1202
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1612
Feature extraction
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Bridges, Gold, Knowledge based systems, Medical services,
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1712
Analytical models, Buildings, Computer architecture, Context,
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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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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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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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Yang, J.,
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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,
Deep convolutional neural network (DCNN), Feature extraction,
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Zhao, G.[Geng],
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V-Factor Indicator in the Assessment of the Change in the
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Zhang, X.,
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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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semantic fusion
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1906
Select a meaningful representative moment from an action performance.
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Zhang, W.J.[Wen-Jie],
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And:
Original:
JVCIR(62), 2019, pp. 117-128.
1908
Machine learning, Multi-cue fusion, Aesthetic tendency, Flickr, Graph mining
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Zeng, H.,
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IEEE DOI
1911
Task analysis, Probabilistic logic, Computational modeling,
Measurement, Predictive models, Training, Explosives,
unified probabilistic formulation
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Zhang, X.,
Gao, X.,
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He, L.,
A Gated Peripheral-Foveal Convolutional Neural Network for Unified
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IEEE DOI
1911
Feature extraction, Logic gates, Visualization,
Convolutional neural networks, Task analysis, Deep learning,
deep learning
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Li, M.[Mao],
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Wang, W.S.[Wen-Shan],
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Neural aesthetic image reviewer,
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1912
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Elsevier DOI
2001
Convolutional neural networks, Fine art,
High-level image features, Wölfflin
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Li, L.,
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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.,
Weakly Supervised Coupled Networks for Visual Sentiment Analysis,
CVPR18(7584-7592)
IEEE DOI
1812
Visualization, Proposals, Feature extraction, Sentiment analysis,
Task analysis, Training, Twitter
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And:
Prediction of Social Image Popularity Dynamics,
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Springer DOI
1909
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2005
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Huang, S.,
Cornelis, B.,
Devolder, B.,
Martens, M.,
Pizurica, A.,
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IEEE DOI
2007
Sparse representation, target detection, paint loss, kernel,
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Ghosal, K.,
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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],
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Deep Palette-based Color Decomposition for Image Recoloring with
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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
BibRef
Hosu, V.[Vlad],
Goldlucke, B.[Bastian],
Saupe, D.[Dietmar],
Effective Aesthetics Prediction With Multi-Level Spatially Pooled
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IEEE DOI
2002
BibRef
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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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
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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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1910
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Martini, M.[Massimo],
Paolanti, M.[Marina],
Pierdicca, R.[Roberto],
Frontoni, E.[Emanuele],
Zingaretti, P.[Primo],
Visual and Textual Sentiment Analysis of Daily News Social Media Images
by Deep Learning,
CIAP19(I:477-487).
Springer DOI
1909
BibRef
Stefanini, M.[Matteo],
Cornia, M.[Marcella],
Baraldi, L.[Lorenzo],
Corsini, M.[Massimiliano],
Cucchiara, R.[Rita],
Artpedia: A New Visual-Semantic Dataset with Visual and Contextual
Sentences in the Artistic Domain,
CIAP19(II:729-740).
Springer DOI
1909
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Offert, F.[Fabian],
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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CVAA18(II:676-691).
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1905
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Gousseau, Y.[Yann],
Ladjal, S.[Said],
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Weakly Supervised Object Detection in Artworks,
CVAA18(II:692-709).
Springer DOI
1905
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Cianci, M.G.,
Molinari, M.,
Information Modeling and Landscape: Intervention Methodology For
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3DARCH19(269-276).
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1904
Aesthetics of landscapes.
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Ma, N.,
Volkov, A.,
Livshits, A.,
Pietrusinski, P.,
Hu, H.,
Bolin, M.,
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).
Springer DOI
1901
BibRef
Apostolidis, K.[Konstantinos],
Mezaris, V.[Vasileios],
Image Aesthetics Assessment Using Fully Convolutional Neural Networks,
MMMod19(I:361-373).
Springer DOI
1901
BibRef
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
BibRef
Liu, W.,
Fu, X.,
Introduce More Characteristics of Samples into Cross-domain Sentiment
Classification,
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
BibRef
Paolanti, M.[Marina],
Kaiser, C.[Carolin],
Schallner, R.[René],
Frontoni, E.[Emanuele],
Zingaretti, P.[Primo],
Visual and Textual Sentiment Analysis of Brand-Related Social Media
Pictures Using Deep Convolutional Neural Networks,
CIAP17(I:402-413).
Springer DOI
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)
IEEE DOI
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
BibRef
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
BibRef
Chen, X.,
Wang, Y.,
Liu, Q.,
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
BibRef
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
1803
Analytical models, Computational modeling, Correlation, Proposals,
Saliency detection, Sentiment analysis, Visualization, saliency,
sentiment perception
BibRef
Niu, T.[Teng],
Zhu, S.[Shiai],
Pang, L.[Lei],
El Saddik, A.[Abdulmotaleb],
Sentiment Analysis on Multi-View Social Data,
MMMod16(II: 15-27).
Springer DOI
1601
BibRef
Shin, A.[Andrew],
Ushiku, Y.[Yoshitaka],
Harada, T.[Tatsuya],
Image Captioning with Sentiment Terms via Weakly-Supervised Sentiment
Dataset,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Rendering Specific Surfaces, Applied Rendering .