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1202
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Clavel, C.,
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Sentiment Analysis: From Opinion Mining to Human-Agent Interaction,
AffCom(7), No. 1, January 2016, pp. 74-93.
IEEE DOI
1603
Analytical models
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Multimodal Sentiment Intensity Analysis in Videos:
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1612
Feature extraction
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Choi, Y.,
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AffCom(8), No. 4, October 2017, pp. 471-479.
IEEE DOI
1712
Bridges, Gold, Knowledge based systems, Medical services,
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Dragoni, M.,
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AffCom(8), No. 4, October 2017, pp. 457-470.
IEEE DOI
1712
Analytical models, Buildings, Computer architecture, Context,
Machine learning, Sentiment analysis, Social network services,
neural networks
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Soleymani, M.[Mohammad],
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Elsevier DOI
1709
Sentiment
BibRef
Campos, V.[Víctor],
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Elsevier DOI
1709
Sentiment
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Sharma, S.[Srishti],
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DOI Link
1709
BibRef
Weichselbraun, A.,
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Scharl, A.,
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
BibRef
Pappas, N.[Nikolaos],
Redi, M.[Miriam],
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1704
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Dragoni, M.,
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OntoSenticNet: A Commonsense Ontology for Sentiment Analysis,
IEEE_Int_Sys(33), No. 3, May 2018, pp. 77-85.
IEEE DOI
1808
Ontologies, Sentiment analysis, Semantics, Task analysis,
Affective computing, Feature extraction, sentiment analysis,
artificial intelligence
BibRef
Liu, A.[Anan],
Shi, Y.D.[Ying-Di],
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visual sentiment distribution prediction,
JVCIR(57), 2018, pp. 243-252.
Elsevier DOI
1812
Using images to express opinions and share experiences.
Image sentiment, Label distribution learning,
Structured sparsity, Low-rank
BibRef
Yang, J.,
She, D.,
Sun, M.,
Cheng, M.,
Rosin, P.L.,
Wang, L.,
Visual Sentiment Prediction Based on Automatic Discovery of Affective
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MultMed(20), No. 9, September 2018, pp. 2513-2525.
IEEE DOI
1809
image representation, learning (artificial intelligence),
neural nets, object tracking, automatic discovery,
visual sentiment analysis
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Ding, X.,
Chen, Z.,
Improving Saliency Detection Based on Modeling Photographer's
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MultMed(21), No. 1, January 2019, pp. 124-134.
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1901
Saliency detection, Visualization, Psychology,
Image color analysis, Task analysis, Feature extraction,
intention rate
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Liu, X.[Xuan],
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Xia, Y.[Yong],
Affective image classification by jointly using interpretable art
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JVCIR(58), 2019, pp. 576-588.
Elsevier DOI
1901
Affective image classification, Discrete emotion space,
Deep convolutional neural network (DCNN), Feature extraction,
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Jin, X.[Xin],
Wu, L.[Le],
Li, X.D.[Xiao-Dong],
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Ge, S.M.[Shi-Ming],
Zhao, G.[Geng],
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ILGNet: inception modules with connected local and global features for
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IET-CV(13), No. 2, March 2019, pp. 206-212.
DOI Link
1902
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Pluta, M.[Magda],
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V-Factor Indicator in the Assessment of the Change in the
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IJGI(8), No. 2, 2019, pp. xx-yy.
DOI Link
1903
BibRef
Zhang, X.,
Li, Z.,
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Pose-Based Composition Improvement for Portrait Photographs,
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IEEE DOI
1903
Painting, Face, Pose estimation, Training, Databases, Electronic mail,
Area measurement, Composition improvement, pose, portrait
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Cui, C.,
Liu, H.,
Lian, T.,
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Zhu, L.,
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Distribution-Oriented Aesthetics Assessment With Semantic-Aware
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MultMed(21), No. 5, May 2019, pp. 1209-1220.
IEEE DOI
1905
computer vision, convolutional neural nets, image representation,
object recognition, image aesthetics assessment,
semantic fusion
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Wang, M.[Meili],
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Liao, M.H.[Ming-Hong],
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Action snapshot with single pose and viewpoint,
VC(35), No. 4, April 2019, pp. 507-520.
Springer DOI
1906
Select a meaningful representative moment from an action performance.
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Zhang, W.J.[Wen-Jie],
Yao, Y.Y.[Yi-Yang],
Wang, J.X.[Jin-Xiong],
Xiang, X.Y.[Xin-Yu],
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Image quality tendency modeling by fusing multiple visual cues,
JVCIR(62), 2019, pp. 117-128.
Elsevier DOI
1908
Machine learning, Multi-cue fusion, Aesthetic tendency, Flickr, Graph mining
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Zeng, H.,
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A Unified Probabilistic Formulation of Image Aesthetic Assessment,
IP(29), No. , 2020, pp. 1548-1561.
IEEE DOI
1911
Task analysis, Probabilistic logic, Computational modeling,
Measurement, Predictive models, Training, Explosives,
unified probabilistic formulation
BibRef
Zhang, X.,
Gao, X.,
Lu, W.,
He, L.,
A Gated Peripheral-Foveal Convolutional Neural Network for Unified
Image Aesthetic Prediction,
MultMed(21), No. 11, November 2019, pp. 2815-2826.
IEEE DOI
1911
Feature extraction, Logic gates, Visualization,
Convolutional neural networks, Task analysis, Deep learning,
deep learning
BibRef
Li, M.[Mao],
Lv, J.C.[Jian-Cheng],
Tang, C.W.[Chen-Wei],
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DOI Link
1912
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Wang, W.S.[Wen-Shan],
Yang, S.[Su],
Zhang, W.S.[Wei-Shan],
Zhang, J.L.[Jiu-Long],
Neural aesthetic image reviewer,
IET-CV(13), No. 8, December 2019, pp. 749-758.
DOI Link
1912
BibRef
Cetinic, E.[Eva],
Lipic, T.[Tomislav],
Grgic, S.[Sonja],
Learning the Principles of Art History with convolutional neural
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PRL(129), 2020, pp. 56-62.
Elsevier DOI
2001
Convolutional neural networks, Fine art,
High-level image features, Wölfflin
BibRef
Li, L.,
Zhu, H.,
Zhao, S.,
Ding, G.,
Lin, W.,
Personality-Assisted Multi-Task Learning for Generic and Personalized
Image Aesthetics Assessment,
IP(29), 2020, pp. 3898-3910.
IEEE DOI
2002
Image aesthetics assessment,
generic and personalized image aesthetics, personality traits,
Siamese network
BibRef
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
Features,
CVPR19(9367-9375).
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
BibRef
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
BibRef
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
BibRef
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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Rodríguez-Pardo, C.[Carlos],
Bilen, H.[Hakan],
Personalised Aesthetics with Residual Adapters,
IbPRIA19(I:508-520).
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1910
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Felicetti, A.[Andrea],
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
Ortis, A.[Alessandro],
Farinella, G.M.[Giovanni Maria],
Battiato, S.[Sebastiano],
Prediction of Social Image Popularity Dynamics,
CIAP19(II:572-582).
Springer DOI
1909
BibRef
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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
BibRef
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
BibRef
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Gousseau, Y.[Yann],
Ladjal, S.[Said],
Bonfait, O.[Olivier],
Weakly Supervised Object Detection in Artworks,
CVAA18(II:692-709).
Springer DOI
1905
BibRef
Cianci, M.G.,
Molinari, M.,
Information Modeling and Landscape: Intervention Methodology For
Reading Complex Systems,
3DARCH19(269-276).
DOI Link
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
BibRef
Saito, J.[Junki],
Nakamura, S.[Satoshi],
Fontender: Interactive Japanese Text Design with Dynamic Font Fusion
Method for Comics,
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
Yang, J.,
She, D.,
Lai, Y.,
Rosin, P.L.,
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
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
BibRef
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
BibRef
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 .