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Ishizuka, M.,
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AffCom(2), No. 1, 2011, pp. 22-36.
IEEE DOI
1202
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Clavel, C.,
Callejas, Z.,
Sentiment Analysis: From Opinion Mining to Human-Agent Interaction,
AffCom(7), No. 1, January 2016, pp. 74-93.
IEEE DOI
1603
Analytical models
BibRef
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Multimodal Sentiment Intensity Analysis in Videos:
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IEEE_Int_Sys(31), No. 6, November 2016, pp. 82-88.
IEEE DOI
1612
Feature extraction
BibRef
Choi, Y.,
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Mihalcea, R.,
Coarse-Grained +/-Effect Word Sense Disambiguation for Implicit
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AffCom(8), No. 4, October 2017, pp. 471-479.
IEEE DOI
1712
Bridges, Gold, Knowledge based systems, Medical services,
Sentiment analysis, Standards, Training data, Sentiment analysis,
word sense disambiguation
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Dragoni, M.,
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A Neural Word Embeddings Approach for Multi-Domain Sentiment Analysis,
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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Elsevier DOI
1709
Sentiment
BibRef
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From pixels to sentiment: Fine-tuning CNNs for visual sentiment
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Elsevier DOI
1709
Sentiment
BibRef
Sharma, S.[Srishti],
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IJCVR(7), No. 5, 2017, pp. 558-573.
DOI Link
1709
BibRef
Weichselbraun, A.,
Gindl, S.,
Fischer, F.,
Vakulenko, S.,
Scharl, A.,
Aspect-Based Extraction and Analysis of Affective Knowledge from
Social Media Streams,
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],
Topkara, M.[Mercan],
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Chang, S.F.[Shih-Fu],
Multilingual visual sentiment concept clustering and analysis,
MultInfoRetr(6), No. 1, March 2017, pp. 51-70.
Springer DOI
1704
BibRef
Dragoni, M.,
Poria, S.,
Cambria, E.,
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
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Liu, A.[Anan],
Shi, Y.D.[Ying-Di],
Jing, P.G.[Pei-Guang],
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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
Regions,
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
Intention,
MultMed(21), No. 1, January 2019, pp. 124-134.
IEEE DOI
1901
Saliency detection, Visualization, Psychology,
Image color analysis, Task analysis, Feature extraction,
intention rate
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Liu, X.[Xuan],
Li, N.[Na],
Xia, Y.[Yong],
Affective image classification by jointly using interpretable art
features and semantic annotations,
JVCIR(58), 2019, pp. 576-588.
Elsevier DOI
1901
Affective image classification, Discrete emotion space,
Deep convolutional neural network (DCNN), Feature extraction,
Support vector machine (SVM)
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Jin, X.[Xin],
Wu, L.[Le],
Li, X.D.[Xiao-Dong],
Zhang, X.K.[Xiao-Kun],
Chi, J.Y.[Jing-Ying],
Peng, S.W.[Si-Wei],
Ge, S.M.[Shi-Ming],
Zhao, G.[Geng],
Li, S.Y.[Shu-Ying],
ILGNet: inception modules with connected local and global features for
efficient image aesthetic quality classification using domain
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IET-CV(13), No. 2, March 2019, pp. 206-212.
DOI Link
1902
BibRef
Pluta, M.[Magda],
Mitka, B.[Bartosz],
V-Factor Indicator in the Assessment of the Change in the
Attractiveness of View as a Result of the Implementation of a
Specific Planning Scenario,
IJGI(8), No. 2, 2019, pp. xx-yy.
DOI Link
1903
BibRef
Zhang, X.,
Li, Z.,
Constable, M.,
Chan, K.L.,
Tang, Z.,
Tang, G.,
Pose-Based Composition Improvement for Portrait Photographs,
CirSysVideo(29), No. 3, March 2019, pp. 653-668.
IEEE DOI
1903
Painting, Face, Pose estimation, Training, Databases, Electronic mail,
Area measurement, Composition improvement, pose, portrait
BibRef
Cui, C.,
Liu, H.,
Lian, T.,
Nie, L.,
Zhu, L.,
Yin, Y.,
Distribution-Oriented Aesthetics Assessment With Semantic-Aware
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IEEE DOI
1905
computer vision, convolutional neural nets, image representation,
object recognition, image aesthetics assessment,
semantic fusion
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Liao, M.H.[Ming-Hong],
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Chang, J.[Jian],
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Action snapshot with single pose and viewpoint,
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Springer DOI
1906
Select a meaningful representative moment from an action performance.
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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).
Springer DOI
1905
BibRef
Gonthier, N.[Nicolas],
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Ladjal, S.[Said],
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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
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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
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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
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 .