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Emotion AI is the ability of computers to understand human emotional states.
Spectral fusion, spectral parsing and the formation of auditory images
Stephen Edward McAdams. 1984 · 1984
Earlier work this paper cites.
The HUMAINE database: Addressing the collection and annotation of naturalistic and induced emotional data. In International Conference on Affective Computing and Intelligent Interaction . Springer, 488–500
Ellen Douglas-Cowie, Roddy Cowie, Ian Sneddon, Cate Cox, Orla Lowry, Margaret Mcrorie, Jean-Claude Martin, Laurence Devillers, Sarkis Abrilian, Anton Batliner, et al · 2007
Earlier work this paper cites.
IEMOCAP: Interactive emotional dyadic motion capture database
Carlos Busso, Murtaza Bulut, Chi-Chun Lee, Abe Kazemzadeh, Emily Mower, Samuel Kim, Jeannette N Chang, Sungbok Lee, and Shrikanth S Narayanan. 2008 · 2008
Earlier work this paper cites.
The Vera am Mittag German audio-visual emotional speech database. In IEEE International Conference on Multimedia and Expo . IEEE, 865–868
Michael Grimm, Kristian Kroschel, and Shrikanth Narayanan. 2008 · 2008
Earlier work this paper cites.
What every body is saying
Julia Navarro and Marvin Karlins. 2008 · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition . Ieee, 248–255
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009
Earlier work this paper cites.
Survey on speech emotion recognition: Features, classification schemes, and databases
Moataz El Ayadi, Mohamed S Kamel, and Fakhri Karray. 2011 · 2011
Earlier work this paper cites.
Deap: A database for emotion analysis; using physiological signals
Sander Koelstra, Christian Muhl, Mohammad Soleymani, Jong-Seok Lee, Ashkan Yazdani, Touradj Ebrahimi, Thierry Pun, Anton Nijholt, and Ioannis Patras. 2011 · 2011
Earlier work this paper cites.
Towards multimodal sentiment analysis: Harvesting opinions from the web. In International Conference on Multimodal Interfaces . 169–176
Louis-Philippe Morency, Rada Mihalcea, and Payal Doshi. 2011 · 2011
Earlier work this paper cites.
Body cues, not facial expressions, discriminate between intense positive and negative emotions
Hillel Aviezer, Yaacov Trope, and Alexander Todorov. 2012 · 2012
Earlier work this paper cites.
Collecting Large, Richly Annotated Facial-Expression Databases from Movies
Abhinav Dhall, Roland Goecke, Simon Lucey, and Tom Gedeon. 2012 · 2012
Earlier work this paper cites.
Recognition of emotion from body language among patients with unipolar depression
Felice Loi, Jatin G Vaidya, and Sergio Paradiso. 2013 · 2013
Earlier work this paper cites.
Affectiva-mit facial expression dataset (am-fed): Naturalistic and spontaneous facial expressions collected. In IEEE Conference on Computer Vision and Pattern Recognition Workshops . 881–888
Daniel McDuff, Rana Kaliouby, Thibaud Senechal, May Amr, Jeffrey Cohn, and Rosalind Picard. 2013 · 2013
Earlier work this paper cites.
Emilya: Emotional body expression in daily actions database.. In LREC . 3486–3493
Nesrine Fourati and Catherine Pelachaud. 2014 · 2014
Earlier work this paper cites.
Emotion recognition and its applications
Agata Kołakowska, Agnieszka Landowska, Mariusz Szwoch, Wioleta Szwoch, and Michal R Wrobel. 2014 · 2014
Earlier work this paper cites.
LIRIS-ACCEDE: A video database for affective content analysis
Yoann Baveye, Emmanuel Dellandrea, Christel Chamaret, and Liming Chen. 2015 · 2015
Earlier work this paper cites.
Video and image based emotion recognition challenges in the wild: Emotiw 2015. In ACM on International Conference on Multimodal Interaction . 423–426
Abhinav Dhall, OV Ramana Murthy, Roland Goecke, Jyoti Joshi, and Tom Gedeon. 2015 · 2015
Cited alongside, same era.
Emotionet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild. In IEEE Conference on Computer Vision and Pattern Recognition . 5562–5570
C Fabian Benitez-Quiroz, Ramprakash Srinivasan, and Aleix M Martinez. 2016 · 2016
Cited alongside, same era.
Retracted: Human emotion recognition based on galvanic skin response signal feature selection and svm. In International Conference on Smart City and Systems Engineering . IEEE, 157–160
Mingyang Liu, Di Fan, Xiaohan Zhang, and Xiaopeng Gong. 2016 · 2016
Cited alongside, same era.
Quo vadis, action recognition? a new model and the kinetics dataset. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 6299–6308
Joao Carreira and Andrew Zisserman. 2017 · 2017
Cited alongside, same era.
Social interaction context shapes emotion recognition through body language, not facial expressions
Lior Abramson, Rotem Petranker, Inbal Marom, and Hillel Aviezer. 2021 · 2021
Later among the works it cites.
Is space-time attention all you need for video understanding?. In ICML , Vol. 2. 4
Gedas Bertasius, Heng Wang, and Lorenzo Torresani. 2021 · 2021
Later among the works it cites.
iMiGUE: An identity-free video dataset for micro-gesture understanding and emotion analysis. In IEEE/CVF Conference on Computer Vision and Pattern Recognition . 10631–10642
Xin Liu, Henglin Shi, Haoyu Chen, Zitong Yu, Xiaobai Li, and Guoying Zhao. 2021 · 2021
Later among the works it cites.
A review on sentiment analysis and emotion detection from text
Pansy Nandwani and Rupali Verma. 2021 · 2021
Later among the works it cites.
Beats: Audio pre-training with acoustic tokenizers
Sanyuan Chen, Yu Wu, Chengyi Wang, Shujie Liu, Daniel Tompkins, Zhuo Chen, and Furu Wei. 2022 · 2022
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Audio set: An ontology and human-labeled dataset for audio events. In 2017 IEEE international conference on acoustics, speech and Signal Processing . IEEE, 776–780
Jort F Gemmeke, Daniel PW Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter. 2017 · 2017
Cited alongside, same era.
Emotion analysis: A survey. In 2017 international conference on computer, communications and electronics (COMPTELIX) . IEEE, 397–402
Nida Manzoor Hakak, Mohsin Mohd, Mahira Kirmani, and Mudasir Mohd. 2017 · 2017
Cited alongside, same era.
Automatic ECG-based emotion recognition in music listening
Yu-Liang Hsu, Jeen-Shing Wang, Wei-Chun Chiang, and Chien-Han Hung. 2017 · 2017
Cited alongside, same era.
Affectnet: A database for facial expression, valence, and arousal computing in the wild
Ali Mollahosseini, Behzad Hasani, and Mohammad H Mahoor. 2017 · 2017
Cited alongside, same era.
Emotion modelling for social robotics applications: a review
Filippo Cavallo, Francesco Semeraro, Laura Fiorini, Gergely Magyar, Peter Sinčák, and Paolo Dario. 2018 · 2018
Cited alongside, same era.
Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph. In Annual Meeting of the Association for Computational Linguistics . 2236–2246
AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency. 2018 · 2018
Cited alongside, same era.
Slowfast networks for video recognition. In Proceedings of the IEEE/CVF international conference on computer vision . 6202–6211
Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. 2019 · 2019
Cited alongside, same era.
Sewa db: A rich database for audio-visual emotion and sentiment research in the wild
Jean Kossaifi, Robert Walecki, Yannis Panagakis, Jie Shen, Maximilian Schmitt, Fabien Ringeval, Jing Han, Vedhas Pandit, Antoine Toisoul, Björn Schuller, et al · 2019
Cited alongside, same era.
Later among the works it cites.
EEG based emotion recognition: A tutorial and review
Xiang Li, Yazhou Zhang, Prayag Tiwari, Dawei Song, Bin Hu, Meihong Yang, Zhigang Zhao, Neeraj Kumar, and Pekka Marttinen. 2022 · 2022
Later among the works it cites.
Video swin transformer. In IEEE/CVF Conference on Computer Vision and Pattern Recognition . 3202–3211
Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu. 2022 · 2022
Later among the works it cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Later among the works it cites.
Emotion recognition from unimodal to multimodal analysis: A review
K Ezzameli and H Mahersia. 2023 · 2023
Later among the works it cites.
Imagebind: One embedding space to bind them all. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 15180–15190
Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra. 2023 · 2023
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Emotion recognition and artificial intelligence: A systematic review (2014–2023) and research recommendations
Smith K Khare, Victoria Blanes-Vidal, Esmaeil S Nadimi, and U Rajendra Acharya. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
Later among the works it cites.
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
Hang Zhang, Xin Li, and Lidong Bing. 2023 · 2023
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The VoicePrivacy 2024 Challenge Evaluation Plan
Natalia Tomashenko, Xiaoxiao Miao, Pierre Champion, Sarina Meyer, Xin Wang, Emmanuel Vincent, Michele Panariello, Nicholas Evans, Junichi Yamagishi, and Massimiliano Todisco. 2024 · 2024
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Vision-language models for vision tasks: A survey
Jingyi Zhang, Jiaxing Huang, Sheng Jin, and Shijian Lu. 2024 · 2024
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