[行爲識別]VideoLSTM Convolves, Attends and Flows for Action Recognition
一、文章主要創新點 1.將乘法變成卷積 用convolution ALSTM(卷積注意力網絡)代替傳統的ALSTM(注意力網絡)，說白了就是把LSTM和soft attention模型中所有的乘法都變成卷積，LSTM網絡的輸入不再是一個向量，而是一個二維的數組，這樣可以保存feature map在空間上的關係。 LSTM內部各個門的更新方程，乘法全部變成卷積 注意力模型，也變成卷積 2.改變att
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- 3. 視頻行爲識別[1]Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
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- 9. 基於深度學習的Action Recognition（行爲識別）【二】
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相關文章
- 1. [行爲識別]RPAN：An end-to-end recurrent pose-attention network for action recognition
- 2. Activity Recognition行爲識別
- 3. 視頻行爲識別[1]Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
- 4. [行爲識別] Two –Stream CNN for Action Recognition in Videos
- 5. 【視頻行爲識別】3D Convolutional Neural Networks for Human Action Recognition：
- 6. 行爲識別 - 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. 基於深度學習的Action Recognition（行爲識別）【二】
- 10. 行爲識別論文閱讀|Temporal Segment Networks: Towards Good Practices for Deep Action Recognition