A Weakly Supervised Approach to Emotion-change Prediction and Improved Mood Inference

Soujanya Narayana, Ibrahim Radwan, Ravikiran Parameshwara, Iman Abbasnejad, Akshay Asthana, Ramanathan Subramanian, Roland Goecke

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

1 Citation (Scopus)

Abstract

Whilst a majority of affective computing research focuses on inferring emotions, examining mood or understanding the mood-emotion interplay has received significantly less attention. Building on prior work, we (a) deduce and incorporate emotion-change (Δ) information for inferring mood, without resorting to annotated labels, and (b) attempt mood prediction for long duration video clips, in alignment with the characterisation of mood. We generate the emotion-change (Δ) labels via metric learning from a pre-trained Siamese Network, and use these in addition to mood labels for mood classification. Experiments evaluating unimodal (training only using mood labels) vs muttimodat (training using mood plus Δ labels) models show that mood prediction benefits from the incorporation of emotion-change information, emphasising the importance of modelling the moodemotion interplay for effective mood inference.

Original languageEnglish
Title of host publication2023 11th International Conference on Affective Computing and Intelligent Interaction, ACII 2023
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-8
Number of pages8
ISBN (Electronic)9798350327434
ISBN (Print)9798350327441
DOIs
Publication statusPublished - 10 Sept 2023
Event11th International Conference on Affective Computing and Intelligent Interaction, ACII 2023 - Cambridge, United States
Duration: 10 Sept 202313 Sept 2023

Publication series

Name2023 11th International Conference on Affective Computing and Intelligent Interaction, ACII 2023

Conference

Conference11th International Conference on Affective Computing and Intelligent Interaction, ACII 2023
Country/TerritoryUnited States
CityCambridge
Period10/09/2313/09/23

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