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(EmotiW2016)Video-based emotion recognition using CNNRNN and C3D hybrid networks
时间 2021-01-11
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深度学习
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C&C++
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Introduction 本文主要利用了RNN和C3D解决视频分类问题,其中RNN将CNN从每个视频帧中提取出来的特征进行时序上的编码,C3D对人脸表征和运动信息同时建模,最后再融合音频特征,完成视频分类。本文以59.02%的正确率较EmotiW 2015 53.8%的正确率高出许多。 Model 整体模型如图1,该模型主要由三个子模型组成:CNN-RNN,C3D和
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相关文章
1.
EmotiW2016第一论文Video-based emotion recognition using CNNRNN and C3D hybrid networks
2.
深度学习文章阅读3--Video-based emotion recognition using CNNRNN and C3D hybrid networks
3.
Emotion Recognition Using Graph Convolutional Networks
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论文阅读-----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》
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10.
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