Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
 Lokale TagsFreigabegeschichteDetailsÜbersicht
  It’s LeVAsa not LevioSA! Latent Encodings for Valence-Arousal Structure Alignment

Nath, S., Udandarao, V., & Shukla, J. (2021). It’s LeVAsa not LevioSA! Latent Encodings for Valence-Arousal Structure Alignment. In J. Haritsa, S. Roy, M. Gupta, S. Mehrotra, B. Srinivasan, & Y. Simmhan (Eds.), CODS COMAD 2021: 8th ACM IKDD CODS and 26th COMAD (pp. 238-242). New York, NY, USA: ACM Press.

Item is

 

einblenden: alle ausblenden: alle

Basisdaten

einblenden: ausblenden:
Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-0009-64AA-7 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0009-64AB-6
Genre: Konferenzbeitrag

Externe Referenzen

einblenden:
ausblenden:
externe Referenz:
https://dl.acm.org/doi/pdf/10.1145/3430984.3431037 (Verlagsversion)
Beschreibung:
-
OA-Status:

Urheber

einblenden:
ausblenden:
 Urheber:
Nath, SS1, Autor           
Udandarao, V, Autor
Shukla, J, Autor
Affiliations:
1External Organizations, ou_persistent22              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: In recent years, great strides have been made in the field of affective computing. Several models have been developed to represent and quantify emotions. Two popular ones include (i) categorical models which represent emotions as discrete labels, and (ii) dimensional models which represent emotions in a Valence-Arousal (VA) circumplex domain. However, there is no standard for annotation mapping between the two labelling methods. We build a novel algorithm for mapping categorical and dimensional model labels using annotation transfer across affective facial image datasets. Further, we utilize the transferred annotations to learn rich and interpretable data representations using a variational autoencoder (VAE). We present “LeVAsa”, a VAE model that learns implicit structure by aligning the latent space with the VA space. We evaluate the efficacy of LeVAsa by comparing performance with the Vanilla VAE using quantitative and qualitative analysis on two benchmark affective image datasets. Our results reveal that LeVAsa achieves high latent-circumplex alignment which leads to improved downstream categorical emotion prediction. The work also demonstrates the trade-off between degree of alignment and quality of reconstructions.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 2021-01
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1145/3430984.3431037
 Art des Abschluß: -

Veranstaltung

einblenden:
ausblenden:
Titel: CODS-COMAD 2021: 8th ACM IKDD CODS and 26th COMAD
Veranstaltungsort: Bangalore, India
Start-/Enddatum: 2021-01-02 - 2021-01-04

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: CODS COMAD 2021: 8th ACM IKDD CODS and 26th COMAD
Genre der Quelle: Konferenzband
 Urheber:
Haritsa, J, Herausgeber
Roy, S, Herausgeber
Gupta, M, Herausgeber
Mehrotra, S, Herausgeber
Srinivasan, BV, Herausgeber
Simmhan, Y, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: New York, NY, USA : ACM Press
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 238 - 242 Identifikator: ISBN: 978-1-4503-8817-7