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

arXiv: https://arxiv.org/abs/2010.03692 · HTML: https://mlsanity.com/paper/2010.03692/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 8 of 8)

- [Analysing Affective Behavior in the second ABAW2 Competition](https://mlsanity.com/paper/2106.15318/) (2021)
- [ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges](https://mlsanity.com/paper/2202.10659/) (2022)
- [ABAW: Learning from Synthetic Data & Multi-Task Learning Challenges](https://mlsanity.com/paper/2207.01138/) (2022)
- [ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challenges](https://mlsanity.com/paper/2303.01498/) (2023)
- [The 6th Affective Behavior Analysis in-the-wild (ABAW) Competition](https://mlsanity.com/paper/2402.19344/) (2024)
- [7th ABAW Competition: Multi-Task Learning and Compound Expression Recognition](https://mlsanity.com/paper/2407.03835/) (2024)
- [Multi-modal Affect Analysis using standardized data within subjects in the Wild](https://mlsanity.com/paper/2107.03009/) (2021)
- [FV2ES: A Fully End2End Multimodal System for Fast Yet Effective Video Emotion Recognition Inference](https://mlsanity.com/paper/2209.10170/) (2022)

## 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%.