Aff-Wild: Valence and Arousal ‘in-the-wild’ Challenge

Zafeiriou, Stefanos, Kollias, Dimitrios, Nicolaou, Mihalis A., Papaioannou, Athanasios, Zhao, Guoying and Kotsia, Irene (2017) Aff-Wild: Valence and Arousal ‘in-the-wild’ Challenge. In: CVPRW 2017: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 21-26 July 2017, Honolulu, USA.

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Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: 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
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 22045
Notes on copyright: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User: Irene Kotsia
Date Deposited: 16 Jun 2017 16:46
Last Modified: 03 Apr 2019 04:20
URI: https://eprints.mdx.ac.uk/id/eprint/22045

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