S. Zafeiriou, D. Kollias, M. A. Nicolaou, A. Papaioannou, G. Zhao and I. Kotsia, "Aff-Wild: Valence and Arousal ‘In-the-Wild’ Challenge," 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, 2017, pp. 1980-1987. doi: 10.1109/CVPRW.2017.248
Aff-wild : valence and arousal ‘in-the-wild’ challenge
|Author:||Zafeiriou, Stefanos1,2; Kollias, Dimitrios1; Nicolaou, Mihalis A.3;|
1Department of Computing, Imperial College London, UK
2Center for Machine Vision and Signal Analysis, University of Oulu, Finland
3Department of Computing, Goldsmiths, University of London, UK
4School of Science and Technology, International Hellenic University, Greece
5Department of Computer Science, Middlesex University, UK
|Online Access:||PDF Full Text (PDF, 2.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe201902276466
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2019-02-27
The Affect-in-the-Wild (Aff-Wild) Challenge proposes a new comprehensive benchmark for assessing the performance of facial affect/behaviour analysis/understanding ‘in-the-wild’. The Aff-wild benchmark contains about 300 videos (over 2,000 minutes of data) annotated with regards to valence and arousal, all captured ‘in-the-wild’ (the main source being Youtube videos). The paper presents the database description, the experimental set up, the baseline method used for the Challenge and finally the summary of the performance of the different methods submitted to the Affect-in-the-Wild Challenge for Valence and Arousal estimation. The challenge demonstrates that meticulously designed deep neural networks can achieve very good performance when trained with in-the-wild data.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops
|Pages:||1980 - 1987|
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops : CVPRW 2017
|Host publication editor:||
IEEE computer society conference on computer vision and pattern recognition workshops
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
The work of Stefanos Zafeiriou has been partially funded by the FiDiPro program of Tekes (project number: 1849/31/2015). The work of Dimitris Kollias was funded by a Teaching Fellowship of Imperial College London. We would like also to acknowledge the contribution of the Youtube users that gave us the permission to use their videos (especially Zalzar and Eddie from The1stTake).
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