11.2.1.3.10 Visual Sentiment Evaluation

Memotion Dataset 7k,
2019.
WWW Link. Dataset, Sentinment. Memotion Dataset. Dataset for sentiment classification of memes.

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
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
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.L.[Huang-Lu], 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.L.[Huang-Lu], 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.W.[Hai-Wei], 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.J.[Li-Juan], Guo, W.[Wenya], Yao, X.X.[Xing-Xu], Zhang, Y.X.[Yu-Xiang], 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

Yang, J.Y.[Jing-Yuan], Gao, X.[Xinbo], Li, L.[Leida], Wang, X.M.[Xiu-Mei], Ding, J.S.[Jin-Shan],
SOLVER: Scene-Object Interrelated Visual Emotion Reasoning Network,
IP(30), 2021, pp. 8686-8701.
IEEE DOI 2110
Visualization, Feature extraction, Psychology, Task analysis, Deep learning, Convolutional neural networks, Semantics, attention mechanism BibRef

Wang, S.[Shangfei], Wang, C.[Can], Chen, T.[Tanfang], Wang, Y.[Yaxin], Shu, Y.Y.[Yang-Yang], Ji, Q.[Qiang],
Video Affective Content Analysis by Exploring Domain Knowledge,
AffCom(12), No. 4, October 2021, pp. 1002-1017.
IEEE DOI 2112
Lighting, Image color analysis, Visualization, Emotion recognition, Feature extraction, Grammar, Music, Content analysis, visual/speech/music elements BibRef

Qi, F.[Fan], Yang, X.[Xiaoshan], Xu, C.S.[Chang-Sheng],
Emotion Knowledge Driven Video Highlight Detection,
MultMed(23), 2021, pp. 3999-4013.
IEEE DOI 2112
Visualization, Training data, Predictive models, Training, Semantics, Emotion recognition, Computational modeling, Deep ranking, video highlight detection BibRef

Long, Y.F.[Yun-Fei], Xiang, R.[Rong], Lu, Q.[Qin], Huang, C.R.[Chu-Ren], Li, M.L.[Ming-Lei],
Improving Attention Model Based on Cognition Grounded Data for Sentiment Analysis,
AffCom(12), No. 4, October 2021, pp. 900-912.
IEEE DOI 2112
Sentiment analysis, Analytical models, Cognition, Data models, Context modeling, Deep learning, Affective lexicons, attention model BibRef

Yao, X.X.[Xing-Xu], Zhao, S.[Sicheng], Lai, Y.K.[Yu-Kun], She, D.[Dongyu], Liang, J.[Jie], Yang, J.F.[Ju-Feng],
APSE: Attention-Aware Polarity-Sensitive Embedding for Emotion-Based Image Retrieval,
MultMed(23), 2021, pp. 4469-4482.
IEEE DOI 2112
BibRef
Earlier: A1, A4, A2, A5, A3, A6:
Attention-Aware Polarity Sensitive Embedding for Affective Image Retrieval,
ICCV19(1140-1150)
IEEE DOI 2004
Visualization, Task analysis, Psychology, Image retrieval, Feature extraction, Image color analysis, Emotion recognition, visual sentiment analysis. affective computing, content-based retrieval, data mining, emotion recognition, image representation, Psychology BibRef

Bisogni, C.[Carmen], Cascone, L.[Lucia], Castiglione, A.[Aniello], Passero, I.[Ignazio],
Deep learning for emotion driven user experiences,
PRL(152), 2021, pp. 115-121.
Elsevier DOI 2112
Deep learning, User emotions, User experience BibRef

Xu, N.[Nan], Mao, W.J.[Wen-Ji], Wei, P.[Penghui], Zeng, D.[Daniel],
MDA: Multimodal Data Augmentation Framework for Boosting Performance on Sentiment/Emotion Classification Tasks,
IEEE_Int_Sys(36), No. 6, November 2021, pp. 3-12.
IEEE DOI 2112
Task analysis, Data analysis, Boosting, Social networking (online), Annotations, Sentiment analysis, Automation, Data augmentation, multimodal classification BibRef

He, J.[Jiaxuan], Hu, H.F.[Hai-Feng],
MF-BERT: Multimodal Fusion in Pre-Trained BERT for Sentiment Analysis,
SPLetters(29), 2022, pp. 454-458.
IEEE DOI 2202
Bit error rate, Visualization, Acoustics, Sentiment analysis, Analytical models, Fuses, Convolution, Internal updating, multimodal fusion BERT BibRef

Wang, H.L.[Han-Li], Tang, P.J.[Peng-Jie], Li, Q.Y.[Qin-Yu], Cheng, M.[Meng],
Emotion Expression With Fact Transfer for Video Description,
MultMed(24), 2022, pp. 715-727.
IEEE DOI 2202
Visualization, Semantics, Sentiment analysis, Measurement, Training, Databases, Video description, Convolutional neural network, video description BibRef

Xiong, X.[Xi], Qiao, S.J.[Shao-Jie], Han, N.[Nan], Li, Y.Y.[Yuan-Yuan], Xiong, F.[Fei], He, L.[Ling],
Affective Impression: Sentiment-Awareness POI Suggestion via Embedding in Heterogeneous LBSNs,
AffCom(13), No. 1, January 2022, pp. 272-284.
IEEE DOI 2203
Heterogeneous networks, Information technology, Social networking (online), Probabilistic logic, Data integrity, probabilistic graphical model BibRef

Wang, B.Q.[Bin-Qiang], Dong, G.[Gang], Zhao, Y.Q.[Ya-Qian], Li, R.G.[Ren-Gang], Cao, Q.C.[Qi-Chun], Chao, Y.Y.[Yin-Yin],
Non-Uniform Attention Network for Multi-modal Sentiment Analysis,
MMMod22(I:612-623).
Springer DOI 2203
BibRef

Bianconi, F., Filippucci, M., Seccaroni, M., Aquinardi, C.M.,
Urban Parametric Perception. the Case Study of the Historic Centre Of Perugia,
ISPRS21(B2-2021: 839-846).
DOI Link 2201
Eye tracking, EEG analysis. BibRef

Liang, Y.[Yun], Maeda, K.[Keisuke], Ogawa, T.[Takahiro], Haseyama, M.[Miki],
Deep Metric Network Via Heterogeneous Semantics for Image Sentiment Analysis,
ICIP21(1039-1043)
IEEE DOI 2201
Measurement, Sentiment analysis, Visualization, Correlation, Design methodology, Semantics, Deep metric learning, heterogeneous semantics BibRef

Can, O.[Ogul], Gürbüz, Y.Z.[Yeti Z.], Alatan, A.A.[A. Aydvn],
Deep Metric Learning With Alternating Projections Onto Feasible Sets,
ICIP21(1264-1268)
IEEE DOI 2201
Measurement, Systematics, Image processing, Image retrieval, Training data, Benchmark testing, Metric learning, projections BibRef

Karaman, K.[Kaan], Alatan, A.A.[A. Aydin],
Metu Loss: Metric Learning With Entangled Triplet Unified Loss,
ICIP21(1279-1283)
IEEE DOI 2201
Measurement, Potential energy, Extraterrestrial phenomena, Image processing, Semantics, Image retrieval, Deep metric learning, image retrieval BibRef

Maqbool, H.[Hira], Masek, M.[Martin],
Image Aesthetics Classification using Deep Features and Image Category,
IVCNZ21(1-5)
IEEE DOI 2201
Deep learning, Industries, Visualization, Automation, Lighting, Manuals, Feature extraction, Image aesthetics, deep learning BibRef

Lian, T.[Tianpei], Cao, Z.G.[Zhi-Guo], Xian, K.[Ke], Pan, Z.[Zhiyu], Zhong, W.[Weicai],
Context-Aware Candidates for Image Cropping,
ICIP21(1479-1483)
IEEE DOI 2201
Convolution, Image processing, Manuals, Real-time systems, Image cropping, fully convolution regression network, aesthetic quality BibRef

Yeo, Y.Y.[Yong-Yaw], See, J.[John], Wong, L.K.[Lai-Kuan], Goh, H.N.[Hui-Ngo],
Generating Aesthetic Based Critique for Photographs,
ICIP21(2523-2527)
IEEE DOI 2201
Measurement, Deep learning, Image processing, Semantics, Decoding, Task analysis, Image captioning, aesthetic quality assessment, Word Mover's Distance BibRef

Lian, T.[Tianpei], Cao, Z.G.[Zhi-Guo], Lu, H.[Hao], Wu, Z.J.[Zi-Jin], Zhong, W.C.[Wei-Cai],
Image Cropping Assisted By Modeling Inter-Patch Relations,
ICIP21(2573-2577)
IEEE DOI 2201
Convolution, Image edge detection, Logic gates, Feature extraction, Task analysis, Faces, Image cropping, aesthetic quality, edge information BibRef

Su, N.M.[Norman Makoto], Crandall, D.J.[David J.],
The Affective Growth of Computer Vision,
CVPR21(9287-9296)
IEEE DOI 2111
Deep learning, Pattern recognition, Standards, Strain BibRef

Singh, J.[Jaskirat], Zheng, L.[Liang],
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings,
CVPR21(16382-16391)
IEEE DOI 2111
Backpropagation, Training, Semantics, Pipelines, Reinforcement learning, Stroke (medical condition) BibRef

Hong, C.Y.[Chao-Yi], Du, S.Y.[Shuai-Yuan], Xian, K.[Ke], Lu, H.[Hao], Cao, Z.G.[Zhi-Guo], Zhong, W.[Weicai],
Composing Photos Like a Photographer,
CVPR21(7053-7062)
IEEE DOI 2111
Photography, Computational modeling, Benchmark testing, Pattern recognition BibRef

Mittal, T.[Trisha], Mathur, P.[Puneet], Bera, A.[Aniket], Manocha, D.[Dinesh],
Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality,
CVPR21(5657-5667)
IEEE DOI 2111
Learning systems, Visualization, Computational modeling, Motion pictures, Pattern recognition, Facial features BibRef

Zhang, Y.M.[Yi-Meng], Zhang, Y.[Yang], Gao, J.J.[Jiao-Jiao],
The Influence of the Aesthetic Design of Taobao APP on Users' Emotional Experience,
DHM21(II:403-414).
Springer DOI 2108
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
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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
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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.S.[Wen-Shan], Qiu, Y.H.[Yu-Heng], Hu, Y.F.[Ya-Fei], Scherer, S.[Sebastian],
Visual Memorability for Robotic Interestingness via Unsupervised Online Learning,
ECCV20(II:52-68).
Springer DOI 2011
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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
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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, 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
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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 Aesthetic Suggestion,
MMMod20(I:127-138).
Springer DOI 2003
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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
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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 BibRef

Wang, W.N.[Wei-Ning], Su, J.J.[Jun-Jie], Li, L.M.[Le-Min], 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
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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
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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 for Art,
CVAA18(II:710-715).
Springer DOI 1905
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Garcia, N.[Noa], Vogiatzis, G.[George],
How to Read Paintings: Semantic Art Understanding with Multi-modal Retrieval,
CVAA18(II:676-691).
Springer DOI 1905
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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
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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
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Apostolidis, K.[Konstantinos], Mezaris, V.[Vasileios],
Image Aesthetics Assessment Using Fully Convolutional Neural Networks,
MMMod19(I:361-373).
Springer DOI 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 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).
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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 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
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Shin, A.[Andrew], Ushiku, Y.[Yoshitaka], Harada, T.[Tatsuya],
Image Captioning with Sentiment Terms via Weakly-Supervised Sentiment Dataset,
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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
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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
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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 .