A Dual Branch Network for Emotional Reaction Intensity Estimation
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Abstract (arXiv)
Emotional Reaction Intensity(ERI) estimation is an important task in multimodal scenarios, and has fundamental applications in medicine, safe driving and other fields. In this paper, we propose a solution to the ERI challenge of the fifth Affective Behavior Analysis in-the-wild(ABAW), a dual-branch based multi-output regression model. The spatial attention is used to better extract visual features, and the Mel-Frequency Cepstral Coefficients technology extracts acoustic features, and a method named modality dropout is added to fusion multimodal features. Our method achieves excellent results on the official validation set.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 26 of its 40 references that are in the map. First posted on arXiv 16 Mar 2023.
Builds on
26 of 40 references.
References · 26
- 2017Attention Is All You Needcited by 59,932
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2016Layer Normalizationcited by 5,579
- 2016Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networkscited by 895
- 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 490
- 2019Multi-Label Image Recognition with Graph Convolutional Networkscited by 320
- 2019Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFacecited by 222
- 2018Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyondcited by 216
- 2020Analysing Affective Behavior in the First ABAW 2020 Competitioncited by 178
14 more
- 2021Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Frameworkcited by 174
- 2021Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Studycited by 168
- 2021Analysing Affective Behavior in the second ABAW2 Competitioncited by 163
- 2022ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challengescited by 156
- 2019Face Behavior a la carte: Expressions, Affect and Action Units in a Single Networkcited by 155
- 2020DFEW: A Large-Scale Database for Recognizing Dynamic Facial Expressions in the Wildcited by 128
- 2023ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challengescited by 110
- 2021Sub-word Level Lip Reading With Visual Attentioncited by 60
- 2021Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognitioncited by 55
- 2022Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wildcited by 41
- 2022The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stresscited by 23
- 2023MMA-MRNNet: Harnessing Multiple Models of Affect and Dynamic Masked RNN for Precise Facial Expression Intensity Estimationcited by 16
- 2022Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment Analysiscited by 12
- 2022Facial Affect Analysis: Learning from Synthetic Data & Multi-Task Learning Challengescited by 10
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