mlsanityData to 7 Oct 2026

An Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wild

arXiv 2010.03692 ↗First posted 7 Oct 2020cs.LG cs.SD eess.AScited by 8

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Abstract (arXiv)

In this paper, we present our contribution to ABAW facial expression challenge. We report the proposed system and the official challenge results adhering to the challenge protocol. Using end-to-end deep learning and benefiting from transfer learning approaches, we reached a test set challenge performance measure of 42.10%.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Oct 2020.

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

8 papers.

  1. 2021Analysing Affective Behavior in the second ABAW2 Competitioncited by 163
  2. 2022ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challengescited by 156
  3. 2022ABAW: Learning from Synthetic Data & Multi-Task Learning Challengescited by 110
  4. 2023ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challengescited by 110
  5. 2024The 6th Affective Behavior Analysis in-the-wild (ABAW) Competitioncited by 78
  6. 20247th ABAW Competition: Multi-Task Learning and Compound Expression Recognitioncited by 53
  7. 2021Multi-modal Affect Analysis using standardized data within subjects in the Wildcited by 4
  8. 2022FV2ES: A Fully End2End Multimodal System for Fast Yet Effective Video Emotion Recognition Inferencecited by 1