Neviarouskaya, A.,
Prendinger, H.,
Ishizuka, M.,
SentiFul: A Lexicon for Sentiment Analysis,
AffCom(2), No. 1, 2011, pp. 22-36.
IEEE DOI
1202
BibRef
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
Zadeh, A.,
Zellers, R.,
Pincus, E.,
Morency, L.P.[Louis-Philippe],
Multimodal Sentiment Intensity Analysis in Videos:
Facial Gestures and Verbal Messages,
IEEE_Int_Sys(31), No. 6, November 2016, pp. 82-88.
IEEE DOI
1612
Feature extraction
BibRef
Choi, Y.,
Wiebe, J.,
Mihalcea, R.,
Coarse-Grained +/-Effect Word Sense Disambiguation for Implicit
Sentiment Analysis,
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
BibRef
Dragoni, M.,
Petrucci, G.,
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
BibRef
Soleymani, M.[Mohammad],
Garcia, D.[David],
Jou, B.[Brendan],
Schuller, B.[Björn],
Chang, S.F.[Shih-Fu],
Pantic, M.[Maja],
A survey of multimodal sentiment analysis,
IVC(65), No. 1, 2017, pp. 3-14.
Elsevier DOI
1709
Sentiment
BibRef
Campos, V.[Víctor],
Jou, B.[Brendan],
Giró-i-Nieto, X.[Xavier],
From pixels to sentiment: Fine-tuning CNNs for visual sentiment
prediction,
IVC(65), No. 1, 2017, pp. 15-22.
Elsevier DOI
1709
Sentiment
BibRef
Sharma, S.[Srishti],
Chakraverty, S.[Shampa],
Sharma, A.[Akhil],
Kaur, J.[Jasleen],
A context-based algorithm for sentiment analysis,
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],
Liu, H.Y.[Hong-Yi],
Jou, B.[Brendan],
Chen, T.[Tao],
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
BibRef
Liu, A.[Anan],
Shi, Y.D.[Ying-Di],
Jing, P.G.[Pei-Guang],
Liu, J.[Jing],
Su, Y.T.[Yu-Ting],
Low-rank regularized multi-view inverse-covariance estimation for
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
BibRef
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
BibRef
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)
BibRef
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
adaptation,
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,
Area measurement, Composition improvement, pose, portrait
BibRef
Cui, C.[Chaoran],
Liu, H.H.[Hui-Hui],
Lian, T.[Tao],
Nie, L.Q.[Li-Qiang],
Zhu, L.[Lei],
Yin, Y.L.[Yi-Long],
Distribution-Oriented Aesthetics Assessment With Semantic-Aware
Hybrid Network,
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
BibRef
Wang, M.[Meili],
Guo, S.[Shihui],
Liao, M.H.[Ming-Hong],
He, D.J.[Dong-Jian],
Chang, J.[Jian],
Zhang, J.J.[Jian-Jun],
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.
BibRef
Zhang, W.J.[Wen-Jie],
Yao, Y.Y.[Yi-Yang],
Wang, J.X.[Jin-Xiong],
Xiang, X.Y.[Xin-Yu],
Shu, P.[Peng],
RETRACTED:
Image quality tendency modeling by fusing multiple visual cues,
JVCIR(69), 2020, pp. 102841.
Elsevier DOI
2006
BibRef
And:
Original:
JVCIR(62), 2019, pp. 117-128.
1908
Machine learning, Multi-cue fusion, Aesthetic tendency, Flickr, Graph mining
BibRef
Zeng, H.,
Cao, Z.,
Zhang, L.,
Bovik, A.C.,
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
Zhang, X.,
Gao, X.,
Lu, W.,
He, L.,
Li, J.,
Beyond Vision: A Multimodal Recurrent Attention Convolutional Neural
Network for Unified Image Aesthetic Prediction Tasks,
MultMed(23), 2021, pp. 611-623.
IEEE DOI
2102
computer vision, convolutional neural nets, feature extraction,
image classification, image enhancement, image fusion,
deep learning
BibRef
Li, M.[Mao],
Lv, J.C.[Jian-Cheng],
Tang, C.W.[Chen-Wei],
Aesthetic assessment of paintings based on visual balance,
IET-IPR(13), No. 14, 12 December 2019, pp. 2821-2828.
DOI Link
1912
BibRef
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
networks,
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
Lu, J.X.[Jia-Xin],
Xu, M.[Mai],
Yang, R.[Ren],
Wang, Z.L.[Zu-Lin],
Understanding and Predicting the Memorability of Outdoor Natural
Scenes,
IP(29), 2020, pp. 4927-4941.
IEEE DOI
2003
Databases, Predictive models, Visualization, Analytical models, Face,
Feature extraction, Correlation, Memorability.
BibRef
She, D.,
Yang, J.,
Cheng, M.,
Lai, Y.,
Rosin, P.L.,
Wang, L.,
WSCNet: Weakly Supervised Coupled Networks for Visual Sentiment
Classification and Detection,
MultMed(22), No. 5, May 2020, pp. 1358-1371.
IEEE DOI
2005
Visualization, Proposals, Task analysis, Feature extraction,
Sentiment analysis, Training, Convolutional neural networks,
convolutional neural networks
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
Ortis, A.[Alessandro],
Farinella, G.M.[Giovanni Maria],
Battiato, S.[Sebastiano],
Survey on visual sentiment analysis,
IET-IPR(14), No. 8, 19 June 2020, pp. 1440-1456.
DOI Link
2005
BibRef
And:
Prediction of Social Image Popularity Dynamics,
CIAP19(II:572-582).
Springer DOI
1909
BibRef
Reddy, G.V.[Gajjala Viswanatha],
Mukherjee, S.[Snehasis],
Thakur, M.[Mainak],
Measuring photography aesthetics with deep CNNs,
IET-IPR(14), No. 8, 19 June 2020, pp. 1561-1570.
DOI Link
2005
BibRef
Huang, S.,
Cornelis, B.,
Devolder, B.,
Martens, M.,
Pizurica, A.,
Multimodal Target Detection by Sparse Coding:
Application to Paint Loss Detection in Paintings,
IP(29), 2020, pp. 7681-7696.
IEEE DOI
2007
Sparse representation, target detection, paint loss, kernel,
multiple imaging modalities
BibRef
Kim, W.,
Choi, J.,
Lee, J.,
Objectivity and Subjectivity in Aesthetic Quality Assessment of
Digital Photographs,
AffCom(11), No. 3, July 2020, pp. 493-506.
IEEE DOI
2008
Quality assessment, Photography, Databases, Visualization, Semantics,
Computational modeling, Image processing, Photograph, subjectivity,
user comment analysis
BibRef
Zhao, L.[Lin],
Shang, M.M.[Mei-Mei],
Gao, F.[Fei],
Li, R.S.[Rong-Sheng],
Huang, F.[Fei],
Yu, J.[Jun],
Representation learning of image composition for aesthetic prediction,
CVIU(199), 2020, pp. 103024.
Elsevier DOI
2009
Photo quality assessment, Image quality assessment,
Deep learning, Aesthetic, Representation learning
BibRef
Zhang, C.[Chao],
Liu, S.[Sitong],
Li, H.[Huizi],
Quality-guided video aesthetics assessment with social media context,
JVCIR(71), 2020, pp. 102643.
Elsevier DOI
2009
Video aesthetic assessment, Structure correlation, SVM
BibRef
Kuang, Q.,
Jin, X.,
Zhao, Q.,
Zhou, B.,
Deep Multimodality Learning for UAV Video Aesthetic Quality
Assessment,
MultMed(22), No. 10, October 2020, pp. 2623-2634.
IEEE DOI
2009
Quality assessment, Cameras, Feature extraction, Photography, Drones,
Streaming media, Aesthetic quality assessment,
deep multimodality learning
BibRef
Liu, J.,
Lv, J.,
Yuan, M.,
Zhang, J.,
Su, Y.,
ABSNet: Aesthetics-Based Saliency Network Using Multi-Task
Convolutional Network,
SPLetters(27), 2020, pp. 2014-2018.
IEEE DOI
2012
Visualization, Task analysis, Feature extraction,
Saliency detection, Signal processing algorithms,
visual saliency detection
BibRef
Li, K.[Ke],
Wu, Y.X.[Yu-Xia],
Xue, Y.[Yao],
Qian, X.M.[Xue-Ming],
Viewpoint Recommendation Based on Object-Oriented 3D Scene
Reconstruction,
MultMed(23), 2021, pp. 257-267.
IEEE DOI
2012
Viewpoints for taking aesthetic photographs of a place-of-interest (POI).
Object detection, Cameras, Feature extraction,
Image reconstruction, Social networking (online),
viewpoint recommendation
BibRef
Ragusa, E.[Edoardo],
Gianoglio, C.[Christian],
Zunino, R.[Rodolfo],
Gastaldo, P.[Paolo],
Image Polarity Detection on Resource-Constrained Devices,
IEEE_Int_Sys(35), No. 6, November 2020, pp. 50-57.
IEEE DOI
2012
Emotional content in the image.
Computer architecture, Feature extraction, Intelligent systems,
Detectors, Object recognition, Computational modeling, Twitter
BibRef
Lotfian, M.[Maryam],
Ingensand, J.[Jens],
Brovelli, M.A.[Maria Antonia],
A Framework for Classifying Participant Motivation that Considers the
Typology of Citizen Science Projects,
IJGI(9), No. 12, 2020, pp. xx-yy.
DOI Link
2012
BibRef
Wen, H.[Huanglu],
You, S.[Shaodi],
Fu, Y.[Ying],
Cross-modal context-gated convolution for multi-modal sentiment
analysis,
PRL(146), 2021, pp. 252-259.
Elsevier DOI
2105
Artificial neural networks, Pattern recognition,
Affective behavior, Multi-modal temporal sequences
BibRef
Wen, H.[Huanglu],
You, S.[Shaodi],
Fu, Y.[Ying],
Cross-modal dynamic convolution for multi-modal emotion recognition,
JVCIR(78), 2021, pp. 103178.
Elsevier DOI
2107
Artificial neural networks, Pattern recognition,
Affective behavior, Multi-modal temporal sequences.
BibRef
Yao, X.X.[Xing-Xu],
She, D.[Dongyu],
Zhang, H.[Haiwei],
Yang, J.F.[Ju-Feng],
Cheng, M.M.[Ming-Ming],
Wang, L.[Liang],
Adaptive Deep Metric Learning for Affective Image Retrieval and
Classification,
MultMed(23), 2021, pp. 1640-1653.
IEEE DOI
2106
Measurement, Visualization, Semantics, Feature extraction,
Task analysis, Image analysis, Image retrieval,
visual sentiment analysis
BibRef
Miyata, M.[Mari],
Aizawa, K.[Kiyoharu],
Estimation of Semantic Impressions from Portraits,
IEICE(E104-D), No. 6, June 2021, pp. 863-872.
WWW Link.
2106
BibRef
He, J.X.[Jia-Xuan],
Mai, S.[Sijie],
Hu, H.F.[Hai-Feng],
A Unimodal Reinforced Transformer With Time Squeeze Fusion for
Multimodal Sentiment Analysis,
SPLetters(28), 2021, pp. 992-996.
IEEE DOI
2106
Sparse matrices, Sentiment analysis, Fuses, Convolution, Kernel,
Analytical models, Visualization, Time squeeze fusion,
multimodal sentiment analysis
BibRef
Peng, W.[Wei],
Hong, X.P.[Xiao-Peng],
Zhao, G.Y.[Guo-Ying],
Adaptive Modality Distillation for Separable Multimodal Sentiment
Analysis,
IEEE_Int_Sys(36), No. 3, May 2021, pp. 82-89.
IEEE DOI
2107
Tensors, Sentiment analysis, Task analysis, Intelligent systems,
Computational modeling, Affective computing, Training data
BibRef
Lin, F.Q.[Fu-Qiang],
Song, Y.P.[Yi-Ping],
Ma, X.[Xingkong],
Min, E.[Erxue],
Liu, B.[Bo],
Sentiment-Aware Emoji Insertion Via Sequence Tagging,
MultMedMag(28), No. 2, April 2021, pp. 40-48.
IEEE DOI
2107
Task analysis, Tagging, Social networking (online),
Sentiment analysis, Blogs, Semantics
BibRef
Wang, L.[Lijuan],
Guo, W.[Wenya],
Yao, X.X.[Xing-Xu],
Zhang, Y.[Yuxiang],
Yang, J.F.[Ju-Feng],
Multimodal Event-Aware Network for Sentiment Analysis in Tourism,
MultMedMag(28), No. 2, April 2021, pp. 49-58.
IEEE DOI
2107
Feature extraction, Blogs, Sentiment analysis, Visualization,
Task analysis, Semantics, Delays
BibRef
Pilarczyk, J.[Joanna],
Janeczko, W.[Weronika],
Sterna, R.[Radoslaw],
Kuniecki, M.[Michal],
Are emotional objects visually salient? The Emotional Maps Database,
JVCIR(79), 2021, pp. 103221.
Elsevier DOI
2109
Meaning maps, Saliency, Emotion, Arousal, Natural scenes, Key objects
BibRef
Yang, M.[Min],
Yin, W.P.[Wen-Peng],
Qu, Q.[Qiang],
Tu, W.T.[Wen-Ting],
Shen, Y.[Ying],
Chen, X.J.[Xiao-Jun],
Neural Attentive Network for Cross-Domain Aspect-Level Sentiment
Classification,
AffCom(12), No. 3, July 2021, pp. 761-775.
IEEE DOI
2109
Adaptation models, Neural networks, Task analysis,
Computational modeling, Probabilistic logic,
multi-view attention
BibRef
Yamamoto, T.[Takahisa],
Takeuchi, S.[Shiki],
Nakazawa, A.[Atsushi],
Image Emotion Recognition Using Visual and Semantic Features Reflecting
Emotional and Similar Objects,
IEICE(E104-D), No. 10, October 2021, pp. 1691-1701.
WWW Link.
2110
BibRef
Thomas, C.[Christopher],
Kovashka, A.[Adriana],
Predicting Visual Political Bias Using Webly Supervised Data and an
Auxiliary Task,
IJCV(129), No. 11, November 2021, pp. 2978-3003.
Springer DOI
2110
BibRef
Thomas, C.[Christopher],
Kovashka, A.[Adriana],
Seeing Behind the Camera: Identifying the Authorship of a Photograph,
CVPR16(3494-3502)
IEEE DOI
1612
180,000 images from 41 well-known photographers.
BibRef
Kayatani, Y.[Yuta],
Yang, Z.[Zekun],
Otani, M.[Mayu],
Garcia, N.[Noa],
Chu, C.[Chenhui],
Nakashima, Y.[Yuta],
Takemura, H.[Haruo],
The Laughing Machine: Predicting Humor in Video,
WACV21(2072-2081)
IEEE DOI
2106
Visualization, TV, Computational modeling,
Face recognition, Predictive models
BibRef
Patro, B.N.[Badri N.],
Lunayach, M.[Mayank],
Srivastava, D.[Deepankar],
Sarvesh, S.[Sarvesh],
Singh, H.[Hunar],
Namboodiri, V.P.[Vinay P.],
Multimodal Humor Dataset: Predicting Laughter tracks for Sitcoms,
WACV21(576-585)
IEEE DOI
WWW Link.
2106
Dataset, Humor. Annotations, Semantics, Bit error rate,
Manuals, Task analysis
BibRef
Tashu, T.M.[Tsegaye Misikir],
Horváth, T.[Tomáš],
Attention-based Multi-Modal Emotion Recognition from Art,
FAPER20(604-612).
Springer DOI
2103
BibRef
Zhang, H.,
Zhang, M.,
Research on Cyberpunk Images in the Visual Digital Media,
CVIDL20(39-43)
IEEE DOI
2102
art, computer literacy, cultural aspects,
educational administrative data processing, human factors,
visual digital media
BibRef
Du, J.,
Li, T.,
The Establishment of Color Proportion and Color Schemes Database of
Shaanxi Fengxiang Wood Engraving New Year Painting,
CVIDL20(305-312)
IEEE DOI
2102
art, image colour analysis, image representation, image scanners,
wood, color schemes database, color value data,
color database
BibRef
Ching, J.H.,
See, J.,
Wong, L.K.,
Learning Image Aesthetics by Learning Inpainting,
ICIP20(2246-2250)
IEEE DOI
2011
Task analysis, Visualization, Loss measurement, Machine learning,
Generative adversarial networks, Generators, Feature extraction,
photographic rules
BibRef
Wang, C.[Chen],
Wang, W.[Wenshan],
Qiu, Y.[Yuheng],
Hu, Y.[Yafei],
Scherer, S.[Sebastian],
Visual Memorability for Robotic Interestingness via Unsupervised Online
Learning,
ECCV20(II:52-68).
Springer DOI
2011
BibRef
Newman, A.[Anelise],
Fosco, C.[Camilo],
Casser, V.[Vincent],
Lee, A.[Allen],
McNamara, B.[Barry],
Oliva, A.[Aude],
Multimodal Memorability: Modeling Effects of Semantics and Decay on
Video Memorability,
ECCV20(XVI: 223-240).
Springer DOI
2010
BibRef
Yang, C.,
Kong, L.,
Research on Product Style Design Based on Genetic Algorithm,
ICIVC20(317-321)
IEEE DOI
2009
Product design, Genetic algorithms, Sociology, Statistics,
Feature extraction, Fatigue, stylized design, genetic algorithm,
co-evolution
BibRef
Pilli, S.,
Patwardhan, M.,
Pedanekar, N.,
Karande, S.,
Predicting Sentiments in Image Advertisements using Semantic
Relations among Sentiment Labels,
EmotioNet20(1640-1648)
IEEE DOI
2008
Semantics, Convolution, Measurement, Feature extraction,
Mathematical model, Neural networks, Visualization
BibRef
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
BibRef
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.,
Zhou, N.,
Lei, P.,
Xu, Y.,
Zheng, Y.,
Fan, J.,
Adaptive Fractional Dilated Convolution Network for Image Aesthetics
Assessment,
CVPR20(14102-14111)
IEEE DOI
2008
Kernel, Convolution, Training, Machine learning, Interpolation,
Task analysis, Libraries
BibRef
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
BibRef
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
BibRef
Kastner, M.A.[Marc A.],
Ide, I.[Ichiro],
Kawanishi, Y.[Yasutomo],
Hirayama, T.[Takatsugu],
Deguchi, D.[Daisuke],
Murase, H.[Hiroshi],
Browsing Visual Sentiment Datasets Using Psycholinguistic Groundings,
MMMod20(II:697-702).
Springer DOI
2003
BibRef
Li, Z.Q.[Zheng-Qing],
Zha, Z.J.[Zheng-Jun],
Cao, Y.[Yang],
Deep Palette-based Color Decomposition for Image Recoloring with
Aesthetic Suggestion,
MMMod20(I:127-138).
Springer DOI
2003
BibRef
Al-Halah, Z.,
Aitken, A.,
Shi, W.,
Caballero, J.,
Smile, Be Happy :) Emoji Embedding for Visual Sentiment Analysis,
CroMoL19(4491-4500)
IEEE DOI
2004
data analysis, data visualisation, image classification,
learning (artificial intelligence), neural nets,
transfer learning
BibRef
Shen, X.[Xi],
Efros, A.A.[Alexei A.],
Aubry, M.[Mathieu],
Discovering Visual Patterns in Art Collections With
Spatially-Consistent Feature Learning,
CVPR19(9270-9279).
IEEE DOI
2002
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.B.[Jie-Bo],
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
BibRef
Rodríguez-Pardo, C.[Carlos],
Bilen, H.[Hakan],
Personalised Aesthetics with Residual Adapters,
IbPRIA19(I:508-520).
Springer DOI
1910
BibRef
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
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
BibRef
Offert, F.[Fabian],
Images of Image Machines. Visual Interpretability in Computer Vision
for Art,
CVAA18(II:710-715).
Springer DOI
1905
BibRef
Garcia, N.[Noa],
Vogiatzis, G.[George],
How to Read Paintings: Semantic Art Understanding with Multi-modal
Retrieval,
CVAA18(II:676-691).
Springer DOI
1905
BibRef
Gonthier, N.[Nicolas],
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.
BibRef
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
Yao, X.,
She, D.,
Zhao, S.,
Liang, J.,
Lai, Y.,
Yang, J.,
Attention-Aware Polarity Sensitive Embedding for Affective Image
Retrieval,
ICCV19(1140-1150)
IEEE DOI
2004
affective computing, content-based retrieval, data mining,
emotion recognition, feature extraction, image representation, Psychology
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
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
Sartori, A.[Andreza],
Senyazar, B.[Berhan],
Salah, A.A.A.[Alkim Almila Akdag],
Salah, A.A.[Albert Ali],
Sebe, N.[Nicu],
Emotions in Abstract Art: Does Texture Matter?,
CIAP15(I:671-682).
Springer DOI
1511
BibRef
Kang, D.W.[Dong-Wann],
Shim, H.[Hyounoh],
Yoon, K.[Kyunghyun],
Mood from painting:
Estimating the mood of painting by using color image scale,
FCV15(1-4)
IEEE DOI
1506
art
BibRef
Gbèhounou, S.[Syntyche],
Lecellier, F.[François],
Can Salient Interest Regions Resume Emotional Impact of an Image?,
CAIP13(515-522).
Springer DOI
1308
BibRef
Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Rendering Specific Surfaces, Applied Rendering .