深度學習文章閱讀3--Video-based emotion recognition using CNNRNN and C3D hybrid networks
Introduction 本文爲EmotiW 2016競賽第一名的論文，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。 Model 整體模型如圖1，該模型主要
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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. （EmotiW2016）Video-based emotion recognition using CNNRNN and C3D hybrid networks
- 3. 論文閱讀-----DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Networks
- 4. Emotion Recognition Using Graph Convolutional Networks
- 5. 文章：Emotion Recognition From Speech With Recurrent Neural Networks
- 6. 深度學習文章閱讀（一）——Alexnet
- 7. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks論文閱讀筆記
- 8. 深度學習文章閱讀1--Very deep convolutional networks for large-scale image recognition
- 9. 深度學習文章閱讀2--3D Convolutional Neural Networks for Human Action Recognition
- 10. 論文閱讀：《Hybrid Code Networks》