Learning supervised scoring ensemble for emotion recognition in the wild
Abstract
State-of-the-art approaches for the previous emotion recognition in the wild challenges are usually built on prevailing Convolutional Neural Networks (CNNs). Although there is clear evidence that CNNs with increased depth or width can usually bring improved predication accuracy, existing top approaches provide supervision only at the output feature layer, resulting in the insufficient training of deep CNN models. In this paper, we present a new learning method named Supervised Scoring Ensemble (SSE) for advancing this challenge with deep CNNs. We first extend the idea of recent deep supervision to deal with emotion recognition problem. Benefiting from adding supervision not only to deep layers but also to intermediate layers and shallow layers, the training of deep CNNs can be well eased. Second, we present a new fusion structure in which class-wise scoring activations at diverse complementary feature layers are concatenated and further used as the inputs for second-level supervision, acting as a deep feature ensemble within a single CNN architecture. We show our proposed learning method brings large accuracy gains over diverse backbone networks consistently. On this year's audio-video based emotion recognition task, the average recognition rate of our best submission is 60.34%, forming a new envelop over all existing records.
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Several challenge-winning studies choose to use both handcrafted and deep features, e.g., by score-level or model-level fusion of separate handcrafted and deep models.
“However, several challenge-winning studies [95], [15], [17], choose to use both handcrafted and deep features, e.g., by scorelevel [95] or model-level [17] fusion of separate handcrafted and deep models.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [95]
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Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
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From individual to group-level emotion recognition: EmotiW 5.0
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Deep learning for human affect recognition: Insights and new developments
Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243 · see citing works
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Multimodal Attention Network for Continuous-Time Emotion Recognition Using Video and EEG Signals
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- Authors
- Hu, P., Cai, D., Wang, S., Yao, A., & Chen, Y.
- Affiliations
- Intel (United States) (US)
- Venue
- Proceedings of the 19th ACM International Conference on Multimodal Interaction
- Publisher
- ACM
- Published
- 2017-11-03
- Pages
- 553-560
- OpenAlex
- W2767618761
- Cited by
- 162
- References
- 7
- Citations by year
- 2025: 5 · 2024: 8 · 2023: 15 · 2022: 21 · 2021: 41 · 2020: 22 · 2019: 32 · 2018: 18
- FWCI
- 22.65
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- deep supervision, deep convolutional neural networks, feature fusion
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