【論文閱讀筆記】Beyond Short Snippets: Deep Networks for Video Classification
主要目的： 視頻分類 測試數據集： Sports-1M、UCF-101 方法概況： 使用在imageNet上預訓練過的CNN（AlexNet或者GoogleLeNet）提取幀級特徵，再將幀級特徵和提取到的光流特徵輸入到池化框架或者LSTM進行訓練，得到分類結果。 主要貢獻： 1.提出採用CNN來得到視頻級的全局描述，並且證明增大幀數能夠顯著提高分類性能。 2.通過在時序上共享參數，參數的數量在特徵
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相關文章
- 1. [翻譯]Beyond Short Snippets: Deep Networks for Video Classification
- 2. 讀書筆記：Beyond Short Snippets: Deep Networks for Video Classification
- 3. Beyond Short Snippets: Deep Networks for Video Classification簡介
- 4. 【論文閱讀筆記】Large-scale Video Classification with Convolutional Neural Networks
- 5. Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification閱讀筆記
- 6. 論文閱讀：《Convolutional Neural Networks for Sentence Classification》
- 7. Machine Learning & Deep Learning 論文閱讀筆記
- 8. 【論文閱讀筆記】Convolutional Neural Networks for Sentence Classification
- 9. 論文筆記--Appearance-and-Relation Networks for Video Classification
- 10. Convolutional Neural Networks for Sentence Classification 閱讀筆記