視頻特徵提取：C3D/Learning Spatiotemporal Features with 3D Convolutional Networks
總結 3D conv在之前並沒有廣爲盛行於視頻處理領域。作者認爲，相比會丟失時間信息的2D conv，3D conv非常適合時空特徵學習（即針對視頻），下圖是2D conv和3D conv的區別。 作者使用基於3x3x3的3D conv的帶有全連接層的深度卷積神經網絡對視頻進行特徵提取，同時驗證了3x3x3的3D conv性能相對其他大小的3D conv更優秀。 並且，訓練好的C3D可以作爲優秀的
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相關文章
- 1. 時空特徵--Learning Spatiotemporal Features with 3D Convolutional Networks
- 2. 【論文閱讀筆記】Learning Spatiotemporal Features with 3D Convolutional Networks
- 3. Learning Spatiotemporal Features with 3D Convolutional Networks 讀書筆記
- 4. 論文筆記C3D：Learning Spatiotemporal Features with 3D Convolutional Networks
- 5. 論文筆記之C3D（Learning Spatiotemporal Features with 3D Convolutional Networks）
- 6. 論文閱讀筆記《leaning spatiotemporal features with 3D convolutional network》
- 7. 論文閱讀筆記（四十）：Learning Spatiotemporal Features with 3D Convolutional Networks（C3D）
- 8. 動作識別經典C3D論文Learning Spatiotemporal Features with 3D Convolutional Networks的介紹
- 9. STSN：Object Detection in Video with Spatiotemporal Sampling Networks
- 10. 【深度學習論文翻譯】Learning Spatiotemporal Features with 3D Convolutional Networks全文對照翻譯