Fetching the paper…
Reading the bibliography…
We introduce FindingEmo, a new image dataset containing annotations for 25k images, specifically tailored to Emotion Recognition.
P. Ekman, “Universal facial expressions of emotion,” California Mental Health Research Digest , vol. 8, no. 4, pp. 151–158, 1970
1970
Earlier work this paper cites.
J. A. Russell, “A circumplex model of affect,” Journal of personality and social psychology , vol. 39, no. 6, pp. 1161–1178, 1980
1980
Earlier work this paper cites.
R. Plutchik, “A general psychoevolutionary theory of emotion,” 1980
1980
Earlier work this paper cites.
A. Mehrabian, “Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament,” Current Psychology: A Journal for Diverse Perspectives on Diverse Psychological Issues , vol. 14, no. 4, pp. 261–292, 1996
1996
Earlier work this paper cites.
R. W. Picard, Affective computing . Cambridge, Mass: MIT Press, 1997
1997
Earlier work this paper cites.
L. F. Barrett, “Discrete emotions or dimensions? the role of valence focus and arousal focus,” Cognition and Emotion , vol. 12, no. 4, pp. 579–599, 1998
1998
Earlier work this paper cites.
M. Lyons, S. Akamatsu, M. Kamachi, and J. Gyoba, “Coding facial expressions with gabor wavelets,” in Proceedings - 3rd IEEE International Conference on Automatic Face and Gesture Recognition, FG 1998 . IEEE, 1998, pp. 200–205
1998
Earlier work this paper cites.
R. Cowie, E. Douglas-Cowie, N. Tsapatsoulis, G. Votsis, S. Kollias, W. Fellenz, and J. Taylor, “Emotion recognition in human-computer interaction,” IEEE Signal Processing Magazine , vol. 18, no. 1, pp. 32–80, 2001
2001
Earlier work this paper cites.
Y.-I. Tian, T. Kanade, and J. Cohn, “Recognizing action units for facial expression analysis,” IEEE transactions on pattern analysis and machine intelligence , vol. 23, no. 2, pp. 97–115, 2001
2001
Earlier work this paper cites.
2003
Earlier work this paper cites.
K. R. Scherer, “What are emotions? and how can they be measured?” SOCIAL SCIENCE INFORMATION SUR LES SCIENCES SOCIALES , vol. 44, no. 4, pp. 695–729, 2005
2005
Earlier work this paper cites.
G. Zhao and M. Pietikainen, “Dynamic texture recognition using local binary patterns with an application to facial expressions,” IEEE transactions on pattern analysis and machine intelligence , vol. 29, no. 6, pp. 915–928, 2007
2007
Earlier work this paper cites.
L. F. Barrett, M. Gendron, and Y.-M. Huang, “Do discrete emotions exist?” Philosophical Psychology , vol. 22, no. 4, pp. 427–437, 2009
2009
Earlier work this paper cites.
P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, “The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression,” in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops . IEEE, 2010, pp. 94–101
2010
Earlier work this paper cites.
S. Zhang, X. Zhao, and B. Lei, “Robust facial expression recognition via compressive sensing,” Sensors (Basel, Switzerland) , vol. 12, no. 3, pp. 3747–3761, 2012
2012
Earlier work this paper cites.
H. Aviezer, Y. Trope, and A. Todorov, “Body cues, not facial expressions, discriminate between intense positive and negative emotions,” Science , vol. 338, no. 6111, pp. 1225–1229, 2012
2012
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” arXiv.org , 2015
2015
Cited alongside, same era.
I. J. Goodfellow, D. Erhan, P. Luc Carrier, A. Courville, M. Mirza, B. Hamner, W. Cukierski, Y. Tang, D. Thaler, D.-H. Lee, Y. Zhou, C. Ramaiah, F. Feng, R. Li, X. Wang, D. Athanasakis, J. Shawe-Taylor, M. Milakov, J. Park, R. Ionescu, M. Popescu, C. Grozea, J. Bergstra, J. Xie, L. Romaszko, B. Xu, Z. Chuang, and Y. Bengio, “Challenges in representation learning: A report on three machine learning contests,” Neural networks , vol. 64, pp. 59–63, 2015
2015
Cited alongside, same era.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings , Y. Bengio and Y. LeCun, Eds., 2015
A. Mollahosseini, B. Hasani, and M. H. Mahoor, “Affectnet: A database for facial expression, valence, and arousal computing in the wild,” IEEE transactions on affective computing , vol. 10, no. 1, pp. 18–31, 2019
2019
Later among the works it cites.
P. A. Kragel, M. C. Reddan, K. S. LaBar, and T. D. Wager, “Emotion schemas are embedded in the human visual system,” Science Advances , vol. 5, no. 7, p. eaaw4358, 2019
2019
Later among the works it cites.
S. M. McKinney, M. Sieniek, V. Godbole, J. Godwin, N. Antropova, H. Ashrafian, T. Back, M. Chesus, G. C. Corrado, A. Darzi, M. Etemadi, F. Garcia-Vicente, F. J. Gilbert, M. Halling-Brown, D. Hassabis, S. Jansen, A. Karthikesalingam, C. J. Kelly, D. King, J. R. Ledsam, D. Melnick, H. Mostofi, L. Peng, J. J. Reicher, B. Romera-Paredes, R. Sidebottom, M. Suleyman, D. Tse, K. C. Young, J. De Fauw, and S. Shetty, “International evaluation of an ai system for breast cancer screening,” Nature (London) , vol. 577, no. 7788, pp. 89–94, 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778
2016
Cited alongside, same era.
E. Harmon-Jones, C. Harmon-Jones, and E. Summerell, “On the importance of both dimensional and discrete models of emotion,” Behavioral Sciences , vol. 7, no. 4, pp. 66–, 2017
2017
Cited alongside, same era.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Commun. ACM , vol. 60, no. 6, p. 84–90, may 2017
2017
Cited alongside, same era.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 2261–2269
2017
Cited alongside, same era.
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba, “Places: A 10 million image database for scene recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017
2017
Cited alongside, same era.
F. Zhang, T. Zhang, Q. Mao, and C. Xu, “Joint pose and expression modeling for facial expression recognition,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . IEEE, 2018, pp. 3359–3368
2018
Cited alongside, same era.
F. Kumfor, A. Ibañez, R. Hutchings, J. L. Hazelton, J. R. Hodges, and O. Piguet, “Beyond the face: how context modulates emotion processing in frontotemporal dementia subtypes,” Brain , vol. 141, no. 4, pp. 1172–1185, 01 2018
2018
Cited alongside, same era.
J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” ArXiv , vol. abs/1804.02767, 2018
2018
Cited alongside, same era.
M. Spezialetti, G. Placidi, and S. Rossi, “Emotion recognition for human-robot interaction: Recent advances and future perspectives,” Frontiers in Robotics and AI , vol. 7, 2020
2020
Later among the works it cites.
J. Zhu, B. Luo, S. Zhao, S. Ying, X. Zhao, and X. Zhao, “Iexpressnet: Facial expression recognition with incremental classes,” in MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia , 2020, pp. 2899–2908
2020
Later among the works it cites.
T. Mittal, P. Guhan, U. Bhattacharya, R. Chandra, A. Bera, and D. Manocha, “Emoticon: Context-aware multimodal emotion recognition using frege’s principle,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020
2020
Later among the works it cites.
S. Zhang, Z. Huang, D. P. Paudel, and L. Van Gool, “Facial emotion recognition with noisy multi-task annotations.” IEEE, 2021
2021
Later among the works it cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever, “Learning transferable visual models from natural language supervision,” 2021
2021
Later among the works it cites.
S. Thuseethan, S. Rajasegarar, and J. Yearwood, “Emosec: Emotion recognition from scene context,” Neurocomputing , vol. 492, pp. 174–187, 2022
2022
Later among the works it cites.
D. Yang, S. Huang, S. Wang, Y. Liu, P. Zhai, L. Su, M. Li, and L. Zhang, “Emotion recognition for multiple context awareness,” in Computer Vision – ECCV 2022 , S. Avidan, G. Brostow, M. Cissé, G. M. Farinella, and T. Hassner, Eds. Cham: Springer Nature Switzerland, 2022, pp. 144–162
2022
Later among the works it cites.
A. Emanuel and E. Eldar, “Emotions as computations,” Neuroscience & Biobehavioral Reviews , vol. 144, p. 104977, 2023
2023
Later among the works it cites.
Z. Y. Huang, C. C. Chiang, J. H. Chen, Y. C. Chen, H. L. Chung, Y. P. Cai, and H. C. Hsu, “A study on computer vision for facial emotion recognition,” Scientific reports , vol. 13, no. 1, pp. 8425–8425, 2023
2023
Later among the works it cites.
T. Darcet, M. Oquab, J. Mairal, and P. Bojanowski, “Vision transformers need registers,” 2023
2023
Later among the works it cites.
S. K. Khare, V. Blanes-Vidal, E. S. Nadimi, and U. R. Acharya, “Emotion recognition and artificial intelligence: A systematic review (2014–2023) and research recommendations,” Information Fusion , vol. 102, p. 102019, 2024
2024
Closest in time.