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Author |
Mikhail Mozerov; V. Kober; I.A. Ovseyevich |
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Title |
Robust Dynamic Programming Algorithm for Motion Detection and Estimation |
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2007 |
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ISE |
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ISE @ ise @ MKO2007 |
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810 |
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Author |
V. Kober; Mikhail Mozerov; Josue Albarez; I.A. Ovseyevich |
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Title |
Algorithms for Impulse Noise Renoval from Corrupted Color Images |
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2007 |
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ISE @ ise @ KMA2007 |
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811 |
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Author |
Carles Fernandez; Xavier Roca; Jordi Gonzalez |
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Title |
Providing Automatic Multilingual Text Generation to Artificial Cognitive Systems |
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2008 |
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ISE @ ise @ FRG2008 |
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1021 |
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Author |
Wenjuan Gong; Zhang Yue; Wei Wang; Cheng Peng; Jordi Gonzalez |
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Title |
Meta-MMFNet: Meta-Learning Based Multi-Model Fusion Network for Micro-Expression Recognition |
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Journal Article |
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2022 |
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ACM Transactions on Multimedia Computing, Communications, and Applications |
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ACMTMC |
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Feature Fusion; Model Fusion; Meta-Learning; Micro-Expression Recognition |
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Abstract |
Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method. |
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May 2022 |
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ISE; 600.157 |
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Admin @ si @ GYW2022 |
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3692 |
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Author |
Wenjuan Gong; Yue Zhang; Wei Wang; Peng Cheng; Jordi Gonzalez |
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Title |
Meta-MMFNet: Meta-learning-based Multi-model Fusion Network for Micro-expression Recognition |
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Journal Article |
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2023 |
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ACM Transactions on Multimedia Computing, Communications, and Applications |
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TMCCA |
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20 |
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2 |
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1–20 |
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Abstract |
Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning-based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method. |
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Admin @ si @ GZW2023 |
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3862 |
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