{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:15:52Z","timestamp":1750220152950,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T00:00:00Z","timestamp":1663113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["021112"],"award-info":[{"award-number":["021112"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,14]]},"DOI":"10.1145\/3549555.3549575","type":"proceedings-article","created":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T16:14:01Z","timestamp":1665159241000},"page":"43-49","source":"Crossref","is-referenced-by-count":4,"title":["Sentiment analysis on 2D images of urban and indoor spaces using deep learning architectures"],"prefix":"10.1145","author":[{"given":"Konstantinos","family":"Chatzistavros","sequence":"first","affiliation":[{"name":"MKLab, CERTH - ITI, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Theodora","family":"Pistola","sequence":"additional","affiliation":[{"name":"MKLab, CERTH - ITI, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sotiris","family":"Diplaris","sequence":"additional","affiliation":[{"name":"MKLab, CERTH - ITI, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Konstantinos","family":"Ioannidis","sequence":"additional","affiliation":[{"name":"MKLab, CERTH - ITI, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefanos","family":"Vrochidis","sequence":"additional","affiliation":[{"name":"MKLab, CERTH - ITI, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ioannis","family":"Kompatsiaris","sequence":"additional","affiliation":[{"name":"MKLab, CERTH - ITI, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,10,7]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"10","volume-title":"Datasets and Current Challenges. 16th International Conference on Signal Processing and Multimedia Applications 54","author":"Ortis Alessandro","year":"2019","unstructured":"Alessandro Ortis & Giovanni Farinella &\u00a0 Sebastiano Battiato . 2019 . An Overview on Image Sentiment Analysis: Methods , Datasets and Current Challenges. 16th International Conference on Signal Processing and Multimedia Applications 54 , 2 (01 2019), 10\u00a0pages. https:\/\/doi.org\/ 10 .5220\/0007909602900300 Alessandro Ortis & Giovanni Farinella &\u00a0Sebastiano Battiato. 2019. An Overview on Image Sentiment Analysis: Methods, Datasets and Current Challenges. 16th International Conference on Signal Processing and Multimedia Applications 54, 2 (01 2019), 10\u00a0pages. https:\/\/doi.org\/10.5220\/0007909602900300"},{"key":"e_1_3_2_1_2_1","unstructured":"Ashutosh Bhawsar Devashish Katoriya Ninad Kapadnis Bhushan Shilawat and Anand Kolapkar. 2020. Automated Sentiment Analysis of Web Multimedia.  Ashutosh Bhawsar Devashish Katoriya Ninad Kapadnis Bhushan Shilawat and Anand Kolapkar. 2020. Automated Sentiment Analysis of Web Multimedia."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2502081.2502282"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/0005-7916(94)90063-9"},{"key":"e_1_3_2_1_5_1","unstructured":"Victor Campos Brendan Jou and Xavier Giro-i Nieto. 2016. From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction. https:\/\/doi.org\/10.48550\/ARXIV.1604.03489  Victor Campos Brendan Jou and Xavier Giro-i Nieto. 2016. From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction. https:\/\/doi.org\/10.48550\/ARXIV.1604.03489"},{"key":"e_1_3_2_1_6_1","unstructured":"Tao Chen Damian Borth Trevor Darrell and Shih-Fu Chang. 2014. DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks. https:\/\/doi.org\/10.48550\/ARXIV.1410.8586  Tao Chen Damian Borth Trevor Darrell and Shih-Fu Chang. 2014. DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks. https:\/\/doi.org\/10.48550\/ARXIV.1410.8586"},{"key":"e_1_3_2_1_7_1","volume-title":"Xception: Deep Learning with Depthwise Separable Convolutions. https:\/\/doi.org\/10.48550\/ARXIV.1610.02357","author":"Chollet Fran\u00e7ois","year":"2016","unstructured":"Fran\u00e7ois Chollet . 2016 . Xception: Deep Learning with Depthwise Separable Convolutions. https:\/\/doi.org\/10.48550\/ARXIV.1610.02357 Fran\u00e7ois Chollet. 2016. Xception: Deep Learning with Depthwise Separable Convolutions. https:\/\/doi.org\/10.48550\/ARXIV.1610.02357"},{"key":"e_1_3_2_1_8_1","volume-title":"The Geneva affective picture database (GAPED): A new 730-picture database focusing on valence and normative significance. Behavior research methods 43 (03","author":"Dan-Glauser Elise","year":"2011","unstructured":"Elise Dan-Glauser and Klaus Scherer . 2011. The Geneva affective picture database (GAPED): A new 730-picture database focusing on valence and normative significance. Behavior research methods 43 (03 2011 ), 468\u201377. https:\/\/doi.org\/10.3758\/s13428-011-0064-1 Elise Dan-Glauser and Klaus Scherer. 2011. The Geneva affective picture database (GAPED): A new 730-picture database focusing on valence and normative significance. Behavior research methods 43 (03 2011), 468\u201377. https:\/\/doi.org\/10.3758\/s13428-011-0064-1"},{"key":"e_1_3_2_1_9_1","first-page":"3","article-title":"OutdoorSent","volume":"38","author":"de Oliveira Wyverson\u00a0Bonasoli","year":"2020","unstructured":"Wyverson\u00a0Bonasoli de Oliveira , Leyza\u00a0Baldo Dorini , Rodrigo Minetto , and Thiago\u00a0 H. Silva . 2020 . OutdoorSent . ACM Transactions on Information Systems 38 , 3 (Jun 2020), 1\u201328. https:\/\/doi.org\/10.1145\/3385186 Wyverson\u00a0Bonasoli de Oliveira, Leyza\u00a0Baldo Dorini, Rodrigo Minetto, and Thiago\u00a0H. Silva. 2020. OutdoorSent. ACM Transactions on Information Systems 38, 3 (Jun 2020), 1\u201328. https:\/\/doi.org\/10.1145\/3385186","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_11_1","volume-title":"Universals and Cultural Differences in the Judgments of Facial Expressions of Emotion. Journal of personality and social psychology 53 (11","author":"Ekman Paul","year":"1987","unstructured":"Paul Ekman , Wallace Friesen , Maureen O\u2019Sullivan , A. Chan , Irene Diacoyanni-Tarlatzis , Karl Heider , Rainer Krause , William LeCompte , Tom Pitcairn , and Pio Ricci\u00a0Bitti . 1987. Universals and Cultural Differences in the Judgments of Facial Expressions of Emotion. Journal of personality and social psychology 53 (11 1987 ), 712\u20137. https:\/\/doi.org\/10.1037\/0022-3514.53.4.712 Paul Ekman, Wallace Friesen, Maureen O\u2019Sullivan, A. Chan, Irene Diacoyanni-Tarlatzis, Karl Heider, Rainer Krause, William LeCompte, Tom Pitcairn, and Pio Ricci\u00a0Bitti. 1987. Universals and Cultural Differences in the Judgments of Facial Expressions of Emotion. Journal of personality and social psychology 53 (11 1987), 712\u20137. https:\/\/doi.org\/10.1037\/0022-3514.53.4.712"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132515.3132520"},{"key":"e_1_3_2_1_14_1","volume-title":"Emotion Recognition in Valence-Arousal Space from Multi-channel EEG data and Wavelet based Deep Learning Framework. Procedia Computer Science 171 (01","author":"Garg Divya","year":"2020","unstructured":"Divya Garg and Gyanendra Verma . 2020. Emotion Recognition in Valence-Arousal Space from Multi-channel EEG data and Wavelet based Deep Learning Framework. Procedia Computer Science 171 (01 2020 ), 857\u2013867. https:\/\/doi.org\/10.1016\/j.procs.2020.04.093 Divya Garg and Gyanendra Verma. 2020. Emotion Recognition in Valence-Arousal Space from Multi-channel EEG data and Wavelet based Deep Learning Framework. Procedia Computer Science 171 (01 2020), 857\u2013867. https:\/\/doi.org\/10.1016\/j.procs.2020.04.093"},{"key":"e_1_3_2_1_15_1","unstructured":"Aur\u00e9lien G\u00e9ron. 2019. Hands-On Machine Learning with Scikit-Learn Keras and TensorFlow. https:\/\/arxiv.org\/abs\/1412.6980  Aur\u00e9lien G\u00e9ron. 2019. Hands-On Machine Learning with Scikit-Learn Keras and TensorFlow. https:\/\/arxiv.org\/abs\/1412.6980"},{"key":"e_1_3_2_1_16_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2015. Deep Residual Learning for Image Recognition. https:\/\/doi.org\/10.48550\/ARXIV.1512.03385  Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2015. Deep Residual Learning for Image Recognition. https:\/\/doi.org\/10.48550\/ARXIV.1512.03385"},{"key":"e_1_3_2_1_17_1","volume-title":"Densely Connected Convolutional Networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society","author":"Huang G.","year":"2017","unstructured":"G. Huang , Z. Liu , L.\u00a0 Van\u00a0Der Maaten , and K.\u00a0 Q. Weinberger . 2017 . Densely Connected Convolutional Networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society , Los Alamitos, CA, USA, 2261\u20132269. https:\/\/doi.org\/10.1109\/CVPR. 2017.243 G. Huang, Z. Liu, L.\u00a0Van\u00a0Der Maaten, and K.\u00a0Q. Weinberger. 2017. Densely Connected Convolutional Networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, Los Alamitos, CA, USA, 2261\u20132269. https:\/\/doi.org\/10.1109\/CVPR.2017.243"},{"key":"e_1_3_2_1_18_1","volume-title":"Kingma and Jimmy Ba","author":"P.","year":"2014","unstructured":"Diederik\u00a0 P. Kingma and Jimmy Ba . 2014 . Adam : A Method for Stochastic Optimization . https:\/\/doi.org\/10.48550\/ARXIV.1412.6980 Diederik\u00a0P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. https:\/\/doi.org\/10.48550\/ARXIV.1412.6980"},{"volume-title":"International affective picture system (IAPS): Instruction manual and affective ratings. The center for research in psychophysiology","author":"Lang J","key":"e_1_3_2_1_19_1","unstructured":"Peter\u00a0 J Lang , Margaret\u00a0 M Bradley , Bruce\u00a0 N Cuthbert , 1999. International affective picture system (IAPS): Instruction manual and affective ratings. The center for research in psychophysiology , University of Florida (1999) . Peter\u00a0J Lang, Margaret\u00a0M Bradley, Bruce\u00a0N Cuthbert, 1999. International affective picture system (IAPS): Instruction manual and affective ratings. The center for research in psychophysiology, University of Florida (1999)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"e_1_3_2_1_21_1","volume-title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. https:\/\/doi.org\/10.48550\/ARXIV.2103.14030","author":"Liu Ze","year":"2021","unstructured":"Ze Liu , Yutong Lin , Yue Cao , Han Hu , Yixuan Wei , Zheng Zhang , Stephen Lin , and Baining Guo . 2021 . Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. https:\/\/doi.org\/10.48550\/ARXIV.2103.14030 Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. 2021. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. https:\/\/doi.org\/10.48550\/ARXIV.2103.14030"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Jana Machajdik and Allan Hanbury. 2010. Affective image classification using features inspired by psychology and art theory. 83\u201392. https:\/\/doi.org\/10.1145\/1873951.1873965  Jana Machajdik and Allan Hanbury. 2010. Affective image classification using features inspired by psychology and art theory. 83\u201392. https:\/\/doi.org\/10.1145\/1873951.1873965","DOI":"10.1145\/1873951.1873965"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Alessandro Ortis Giovanni Farinella Giovanni Torrisi and Sebastiano Battiato. 2018. Visual Sentiment Analysis Based on on Objective Text Description of Images. 1\u20136. https:\/\/doi.org\/10.1109\/CBMI.2018.8516481  Alessandro Ortis Giovanni Farinella Giovanni Torrisi and Sebastiano Battiato. 2018. Visual Sentiment Analysis Based on on Objective Text Description of Images. 1\u20136. https:\/\/doi.org\/10.1109\/CBMI.2018.8516481","DOI":"10.1109\/CBMI.2018.8516481"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-013-0695-z"},{"key":"e_1_3_2_1_25_1","volume-title":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 860\u2013868","author":"Peng Kuan-Chuan","year":"2015","unstructured":"Kuan-Chuan Peng , Tsuhan Chen , Amir Sadovnik , and Andrew Gallagher . 2015 . A mixed bag of emotions: Model, predict, and transfer emotion distributions . In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 860\u2013868 . https:\/\/doi.org\/10.1109\/CVPR.2015.7298687 Kuan-Chuan Peng, Tsuhan Chen, Amir Sadovnik, and Andrew Gallagher. 2015. A mixed bag of emotions: Model, predict, and transfer emotion distributions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 860\u2013868. https:\/\/doi.org\/10.1109\/CVPR.2015.7298687"},{"volume-title":"a psychoevolutionary synthesis \/ Robert Plutchik","key":"e_1_3_2_1_26_1","unstructured":"Robert. Plutchik. 1980. Emotion , a psychoevolutionary synthesis \/ Robert Plutchik . Harper & Row , New York . Robert. Plutchik. 1980. Emotion, a psychoevolutionary synthesis \/ Robert Plutchik.Harper & Row, New York."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Joseph Redmon and Ali Farhadi. 2016. YOLO9000: Better Faster Stronger. https:\/\/doi.org\/10.48550\/ARXIV.1612.08242  Joseph Redmon and Ali Farhadi. 2016. YOLO9000: Better Faster Stronger. https:\/\/doi.org\/10.48550\/ARXIV.1612.08242","DOI":"10.1109\/CVPR.2017.690"},{"key":"e_1_3_2_1_28_1","volume-title":"The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 3234\u20133243","author":"Ros German","year":"2016","unstructured":"German Ros , Laura Sellart , Joanna Materzynska , David Vazquez , and Antonio\u00a0 M. Lopez . 2016 . The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 3234\u20133243 . https:\/\/doi.org\/10.1109\/CVPR.2016.352 German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio\u00a0M. Lopez. 2016. The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 3234\u20133243. https:\/\/doi.org\/10.1109\/CVPR.2016.352"},{"key":"e_1_3_2_1_29_1","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. https:\/\/doi.org\/10.48550\/ARXIV.1409.1556  Karen Simonyan and Andrew Zisserman. 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. https:\/\/doi.org\/10.48550\/ARXIV.1409.1556"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens and Zbigniew Wojna. 2015. Rethinking the Inception Architecture for Computer Vision. https:\/\/doi.org\/10.48550\/ARXIV.1512.00567  Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens and Zbigniew Wojna. 2015. Rethinking the Inception Architecture for Computer Vision. https:\/\/doi.org\/10.48550\/ARXIV.1512.00567","DOI":"10.1109\/CVPR.2016.308"},{"key":"e_1_3_2_1_31_1","volume-title":"Cross-Media Learning for Image Sentiment Analysis in the Wild. In 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). 308\u2013317","author":"Vadicamo Lucia","year":"2017","unstructured":"Lucia Vadicamo , Fabio Carrara , Andrea Cimino , Stefano Cresci , Felice Dell\u2019Orletta , Fabrizio Falchi , and Maurizio Tesconi . 2017 . Cross-Media Learning for Image Sentiment Analysis in the Wild. In 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). 308\u2013317 . https:\/\/doi.org\/10.1109\/ICCVW.2017.45 Lucia Vadicamo, Fabio Carrara, Andrea Cimino, Stefano Cresci, Felice Dell\u2019Orletta, Fabrizio Falchi, and Maurizio Tesconi. 2017. Cross-Media Learning for Image Sentiment Analysis in the Wild. In 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). 308\u2013317. https:\/\/doi.org\/10.1109\/ICCVW.2017.45"},{"key":"e_1_3_2_1_32_1","unstructured":"Can Xu Suleyman Cetintas Kuang-Chih Lee and Li-Jia Li. 2014. Visual Sentiment Prediction with Deep Convolutional Neural Networks. https:\/\/doi.org\/10.48550\/ARXIV.1411.5731  Can Xu Suleyman Cetintas Kuang-Chih Lee and Li-Jia Li. 2014. Visual Sentiment Prediction with Deep Convolutional Neural Networks. https:\/\/doi.org\/10.48550\/ARXIV.1411.5731"},{"key":"e_1_3_2_1_33_1","volume-title":"Sentiment analysis of social images via hierarchical deep fusion of content and links. Applied Soft Computing 80 (04","author":"Xu Jie","year":"2019","unstructured":"Jie Xu , Feiran Huang , Xiaoming Zhang , Senzhang Wang , Chaozhuo Li , Zhoujun Li , and Yueying He. 2019. Sentiment analysis of social images via hierarchical deep fusion of content and links. Applied Soft Computing 80 (04 2019 ). https:\/\/doi.org\/10.1016\/j.asoc.2019.04.010 Jie Xu, Feiran Huang, Xiaoming Zhang, Senzhang Wang, Chaozhuo Li, Zhoujun Li, and Yueying He. 2019. Sentiment analysis of social images via hierarchical deep fusion of content and links. Applied Soft Computing 80 (04 2019). https:\/\/doi.org\/10.1016\/j.asoc.2019.04.010"},{"key":"e_1_3_2_1_34_1","volume-title":"Architectural Form and Affect: A Spatiotemporal Study of Arousal. In 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE. https:\/\/doi.org\/10","author":"Xylakis Emmanouil","year":"2021","unstructured":"Emmanouil Xylakis , Antonios Liapis , and Georgios\u00a0 N. Yannakakis . 2021 . Architectural Form and Affect: A Spatiotemporal Study of Arousal. In 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE. https:\/\/doi.org\/10 .1109\/acii52823.2021.9597420 Emmanouil Xylakis, Antonios Liapis, and Georgios\u00a0N. Yannakakis. 2021. Architectural Form and Affect: A Spatiotemporal Study of Arousal. In 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE. https:\/\/doi.org\/10.1109\/acii52823.2021.9597420"},{"key":"e_1_3_2_1_35_1","first-page":"9","article-title":"Visual Sentiment Prediction Based on Automatic Discovery of Affective","volume":"20","author":"Yang Jufeng","year":"2018","unstructured":"Jufeng Yang , Dongyu She , Ming Sun , Ming-Ming Cheng , Paul\u00a0 L. Rosin , and Liang Wang . 2018 . Visual Sentiment Prediction Based on Automatic Discovery of Affective Regions. Trans. Multi. 20 , 9 (sep 2018), 2513\u20132525. https:\/\/doi.org\/10.1109\/TMM.2018.2803520 Jufeng Yang, Dongyu She, Ming Sun, Ming-Ming Cheng, Paul\u00a0L. Rosin, and Liang Wang. 2018. Visual Sentiment Prediction Based on Automatic Discovery of Affective Regions. Trans. Multi. 20, 9 (sep 2018), 2513\u20132525. https:\/\/doi.org\/10.1109\/TMM.2018.2803520","journal-title":"Regions. Trans. Multi."},{"key":"e_1_3_2_1_36_1","volume-title":"Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks. (09","author":"You Quanzeng","year":"2015","unstructured":"Quanzeng You , Jiebo Luo , Hailin Jin , and Jianchao Yang . 2015. Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks. (09 2015 ), 381\u2013388. Quanzeng You, Jiebo Luo, Hailin Jin, and Jianchao Yang. 2015. Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks. (09 2015), 381\u2013388."},{"key":"e_1_3_2_1_37_1","unstructured":"Quanzeng You Jiebo Luo Hailin Jin and Jianchao Yang. 2016. Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark. https:\/\/doi.org\/10.48550\/ARXIV.1605.02677  Quanzeng You Jiebo Luo Hailin Jin and Jianchao Yang. 2016. Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark. https:\/\/doi.org\/10.48550\/ARXIV.1605.02677"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Jianbo Yuan Sean Mcdonough Quanzeng You and Jiebo Luo. 2013. Sentribute: image sentiment analysis from a mid-level perspective. https:\/\/doi.org\/10.1145\/2502069.2502079  Jianbo Yuan Sean Mcdonough Quanzeng You and Jiebo Luo. 2013. Sentribute: image sentiment analysis from a mid-level perspective. https:\/\/doi.org\/10.1145\/2502069.2502079","DOI":"10.1145\/2502069.2502079"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Qiang Zhang Xianxiang Chen Qingyuan Zhan Ting Yang and Shanhong Xia. 2017. Respiration-based emotion recognition with deep learning. Comput. Ind. 92-93(2017) 84\u201390.  Qiang Zhang Xianxiang Chen Qingyuan Zhan Ting Yang and Shanhong Xia. 2017. Respiration-based emotion recognition with deep learning. Comput. Ind. 92-93(2017) 84\u201390.","DOI":"10.1016\/j.compind.2017.04.005"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2019.2928998"},{"key":"e_1_3_2_1_41_1","volume-title":"An image-text consistency driven multimodal sentiment analysis approach for social media. Information Processing & Management 56 (08","author":"Zhao Ziyuan","year":"2019","unstructured":"Ziyuan Zhao , Huiying Zhu , Zehao Xue , Zhao Liu , Jing Tian , Matthew Chua , and Maofu Liu . 2019. An image-text consistency driven multimodal sentiment analysis approach for social media. Information Processing & Management 56 (08 2019 ). https:\/\/doi.org\/10.1016\/j.ipm.2019.102097 Ziyuan Zhao, Huiying Zhu, Zehao Xue, Zhao Liu, Jing Tian, Matthew Chua, and Maofu Liu. 2019. An image-text consistency driven multimodal sentiment analysis approach for social media. Information Processing & Management 56 (08 2019). https:\/\/doi.org\/10.1016\/j.ipm.2019.102097"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2723009"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Bolei Zhou Hang Zhao Xavier Puig Tete Xiao Sanja Fidler Adela Barriuso and Antonio Torralba. 2016. Semantic Understanding of Scenes through the ADE20K Dataset. https:\/\/doi.org\/10.48550\/ARXIV.1608.05442  Bolei Zhou Hang Zhao Xavier Puig Tete Xiao Sanja Fidler Adela Barriuso and Antonio Torralba. 2016. Semantic Understanding of Scenes through the ADE20K Dataset. https:\/\/doi.org\/10.48550\/ARXIV.1608.05442","DOI":"10.1109\/CVPR.2017.544"}],"event":{"name":"CBMI 2022: International Conference on Content-based Multimedia Indexing","acronym":"CBMI 2022","location":"Graz Austria"},"container-title":["International Conference on Content-based Multimedia Indexing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3549555.3549575","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3549555.3549575","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:12Z","timestamp":1750186812000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3549555.3549575"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,14]]},"references-count":43,"alternative-id":["10.1145\/3549555.3549575","10.1145\/3549555"],"URL":"https:\/\/doi.org\/10.1145\/3549555.3549575","relation":{},"subject":[],"published":{"date-parts":[[2022,9,14]]}}}