[行为识别]VideoLSTM Convolves, Attends and Flows for Action Recognition

时间 2021-01-02

标签行为识别繁體版

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一、文章主要创新点 1.将乘法变成卷积用convolution ALSTM(卷积注意力网络)代替传统的ALSTM(注意力网络)，说白了就是把LSTM和soft attention模型中所有的乘法都变成卷积，LSTM网络的输入不再是一个向量，而是一个二维的数组，这样可以保存feature map在空间上的关系。 LSTM内部各个门的更新方程，乘法全部变成卷积注意力模型，也变成卷积 2.改变att

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1. [行为识别]RPAN：An end-to-end recurrent pose-attention network for action recognition
2. Activity Recognition行为识别
3. 视频行为识别[1]Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
4. [行为识别] Two –Stream CNN for Action Recognition in Videos
5. 【视频行为识别】3D Convolutional Neural Networks for Human Action Recognition：
6. 行为识别 - Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition
7. StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
8. Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
9. 基于深度学习的Action Recognition（行为识别）【二】
10. 行为识别论文阅读|Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
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1. [行为识别]RPAN：An end-to-end recurrent pose-attention network for action recognition
2. Activity Recognition行为识别
3. 视频行为识别[1]Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
4. [行为识别] Two –Stream CNN for Action Recognition in Videos
5. 【视频行为识别】3D Convolutional Neural Networks for Human Action Recognition：
6. 行为识别 - Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition
7. StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
8. Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
9. 基于深度学习的Action Recognition（行为识别）【二】
10. 行为识别论文阅读|Temporal Segment Networks: Towards Good Practices for Deep Action Recognition

>>更多相关文章<<