mlsanityData to 14 Oct 2026

Multi-modal Emotion Estimation for in-the-wild Videos

arXiv 2203.13032 ↗First posted 24 Mar 2022cs.CV eess.IVcited by 15

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Abstract (arXiv)

In this paper, we briefly introduce our submission to the Valence-Arousal Estimation Challenge of the 3rd Affective Behavior Analysis in-the-wild (ABAW) competition. Our method utilizes the multi-modal information, i.e., the visual and audio information, and employs a temporal encoder to model the temporal context in the videos. Besides, a smooth processor is applied to get more reasonable predictions, and a model ensemble strategy is used to improve the performance of our proposed method. The experiment results show that our method achieves 65.55% ccc for valence and 70.88% ccc for arousal on the validation set of the Aff-Wild2 dataset, which prove the effectiveness of our proposed method.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 19 of its 32 references that are in the map. First posted on arXiv 24 Mar 2022.

Builds on

19 of 32 references.

References · 19

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7 more
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Citing papers per year: 2022: 2; 2023: 6; 2024: 5; 2025: 2.

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