mlsanityData to 7 Oct 2026

Technical Report for Valence-Arousal Estimation on Affwild2 Dataset

arXiv 2105.01502 ↗First posted 4 May 2021cs.CVcited by 8

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Abstract (arXiv)

In this work, we describe our method for tackling the valence-arousal estimation challenge from ABAW FG-2020 Competition. The competition organizers provide an in-the-wild Aff-Wild2 dataset for participants to analyze affective behavior in real-life settings. We use MIMAMO Net citedeng2020mimamo model to achieve information about micro-motion and macro-motion for improving video emotion recognition and achieve Concordance Correlation Coefficient (CCC) of 0.415 and 0.511 for valence and arousal on the reselected validation set.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 7 of its 13 references that are in the map. First posted on arXiv 4 May 2021.

Builds on

7 of 13 references.

References · 7

  1. 2019Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFacecited by 222
  2. 2018Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyondcited by 216
  3. 2020Analysing Affective Behavior in the First ABAW 2020 Competitioncited by 178
  4. 2019Face Behavior a la carte: Expressions, Affect and Action Units in a Single Networkcited by 155
  5. 2020Multitask Emotion Recognition with Incomplete Labelscited by 39
  6. 2018The OMG-Emotion Behavior Datasetcited by 25
  7. 2019MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion Recognitioncited by 17

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Citing papers per year: 2021: 3; 2022: 2; 2023: 1; 2024: 2.

8 papers.