research-article

Efficient Labelling of Affective Video Datasets via Few-Shot & Multi-Task Contrastive Learning

Authors:

Ravikiran Parameshwara,

Ibrahim Radwan,

Akshay Asthana,

Iman Abbasnejad,

Ramanathan Subramanian,

Roland GoeckeAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 6161 - 6170

https://doi.org/10.1145/3581783.3613784

Published: 27 October 2023 Publication History

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Efficient Labelling of Affective Video Datasets via Few-Shot & Multi-Task Contrastive Learning

Pages 6161 - 6170

Abstract
References

Abstract

Whilst deep learning techniques have achieved excellent emotion prediction, they still require large amounts of labelled training data, which are (a) onerous and tedious to compile, and (b) prone to errors and biases. We propose Multi-Task Contrastive Learning for Affect Representation (MT-CLAR) for few-shot affect inference. MT-CLAR combines multi-task learning with a Siamese network trained via contrastive learning to infer from a pair of expressive facial images (a) the (dis)similarity between the facial expressions, and (b) the difference in valence and arousal levels of the two faces. We further extend the image-based MT-CLAR framework for automated video labelling where, given one or a few labelled video frames (termed support-set), MT-CLAR labels the remainder of the video for valence and arousal. Experiments are performed on the AFEW-VA dataset with multiple support-set configurations; moreover, supervised learning on representations learnt via MT-CLAR are used for valence, arousal and categorical emotion prediction on the AffectNet and AFEW-VA datasets. The results show that valence and arousal predictions via MT-CLAR are very comparable to the state-of-the-art (SOTA), and we significantly outperform SOTA with a support-set ≈6% the size of the video dataset.

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Cited By

Parameshwara RNarayana SMurugappan MRadwan IGoecke RSubramanian R(2024)Exploring Electroencephalography-Based Affective Analysis and Detection of Parkinson’s DiseaseIntelligent Computing10.34133/icomputing.00843Online publication date: 17-Oct-2024
https://doi.org/10.34133/icomputing.0084
Sharma GDhall ASubramanian R(2024)MARS: A Multiview Contrastive Approach to Human Activity Recognition From Accelerometer SensorIEEE Sensors Letters10.1109/LSENS.2024.33579418:3(1-4)Online publication date: Mar-2024
https://doi.org/10.1109/LSENS.2024.3357941
Wang HWang RZou PNi QSun X(2024)MCAN: An Efficient Multi-Task Network for Facial Expression Analysis2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD61410.2024.10580014(1037-1042)Online publication date: 8-May-2024
https://doi.org/10.1109/CSCWD61410.2024.10580014
Show More Cited By

Index Terms

Efficient Labelling of Affective Video Datasets via Few-Shot & Multi-Task Contrastive Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

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Published: 27 October 2023

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MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

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Cited By

Parameshwara RNarayana SMurugappan MRadwan IGoecke RSubramanian R(2024)Exploring Electroencephalography-Based Affective Analysis and Detection of Parkinson’s DiseaseIntelligent Computing10.34133/icomputing.00843Online publication date: 17-Oct-2024
https://doi.org/10.34133/icomputing.0084
Sharma GDhall ASubramanian R(2024)MARS: A Multiview Contrastive Approach to Human Activity Recognition From Accelerometer SensorIEEE Sensors Letters10.1109/LSENS.2024.33579418:3(1-4)Online publication date: Mar-2024
https://doi.org/10.1109/LSENS.2024.3357941
Wang HWang RZou PNi QSun X(2024)MCAN: An Efficient Multi-Task Network for Facial Expression Analysis2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD61410.2024.10580014(1037-1042)Online publication date: 8-May-2024
https://doi.org/10.1109/CSCWD61410.2024.10580014
Berlincioni LCultrera LBecattini FBimbo A(2024)Neuromorphic valence and arousal estimationJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-024-04885-wOnline publication date: 26-Oct-2024
https://doi.org/10.1007/s12652-024-04885-w

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