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EmotiW2016第一論文Video-based emotion recognition using CNNRNN and C3D hybrid networks
時間 2021-01-11
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深度學習
EmotiW
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C&C++
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這篇論文主要利用了RNN和C3D解決視頻分類問題,其中RNN將CNN從每個視頻幀中提取出來的特徵進行時序上的編碼,C3D對人臉表徵和運動信息同時建模,最後再融合音頻特徵,完成視頻分類。本文以59.02%的正確率較EmotiW 2015 53.8%的正確率高出許多。 整體模型如圖1,該模型主要由三個子模型組成:CNN-RNN,C3D和音頻模型;CNN-RNN和C3D模型較爲核心。本文單獨訓練三
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相關文章
1.
(EmotiW2016)Video-based emotion recognition using CNNRNN and C3D hybrid networks
2.
深度學習文章閱讀3--Video-based emotion recognition using CNNRNN and C3D hybrid networks
3.
Emotion Recognition Using Graph Convolutional Networks
4.
論文閱讀-----DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Networks
5.
2018 Interspeech On Enhancing Speech Emotion Recognition using Generative Adversarial Networks
6.
文章:Emotion Recognition From Speech With Recurrent Neural Networks
7.
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks 論文筆記
8.
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks論文閱讀筆記
9.
論文筆記:OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
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OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
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