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Emotional Reaction Intensity Estimation Based on Multimodal Data

arXiv 2303.09167 ↗First posted 16 Mar 2023cs.CV cs.AIcited by 0

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Abstract (arXiv)

This paper introduces our method for the Emotional Reaction Intensity (ERI) Estimation Challenge, in CVPR 2023: 5th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). Based on the multimodal data provided by the originazers, we extract acoustic and visual features with different pretrained models. The multimodal features are mixed together by Transformer Encoders with cross-modal attention mechnism. In this paper, 1. better features are extracted with the SOTA pretrained models. 2. Compared with the baseline, we improve the Pearson's Correlations Coefficient a lot. 3. We process the data with some special skills to enhance performance ability of our model.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 21 references that are in the map. First posted on arXiv 16 Mar 2023.

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References · 17

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5 more
  1. 2023ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challengescited by 110
  2. 2020Self-training and Pre-training are Complementary for Speech Recognitioncited by 98
  3. 2021Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognitioncited by 55
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  5. 2021Facial expression and attributes recognition based on multi-task learning of lightweight neural networkscited by 40

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