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(EmotiW2016)Video-based emotion recognition using CNNRNN and C3D hybrid networks
時間 2021-01-11
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深度學習
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EmotiW
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C&C++
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Introduction 本文主要利用了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。 Model 整體模型如圖1,該模型主要由三個子模型組成:CNN-RNN,C3D和
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相關文章
1.
EmotiW2016第一論文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.
SemEval2019Task3_ERC | (6) Hybrid Features for Emotion Recognition in Textual Conversation
7.
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
8.
【OverFeat】《OverFeat:Integrated Recognition, Localization and Detection using Convolutional Networks》
9.
Multimodal Gesture Recognition Using 3-D Convolution and Convolutional LSTM
10.
Recurrent Neural Networks for Emotion Recognition in Video
>>更多相關文章<<