Two-Stream Convolutional Networks for Action Recognition in Videos（summary part）
算法介紹 雙流網絡使用以單幀RGB作爲輸入的CNN來處理空間維度的信息，使用以多幀密度光流場作爲輸入的CNN來處理時間維度的信息，並通過多任務訓練的方法將兩個行爲分類的數據集聯合起來(UCF101與HMDB)，去除過擬合進而獲得更好效果。 貢獻 提出two-stream ConvNet來對時空特徵進行建模表示 提出了多幀光流作爲輸入，對性能提升作用很大 源碼 未公開源碼 光流 圖像中物體的運動可以
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相關文章
- 1. 譯：Two-stream convolutional networks for action recognition in videos
- 2. 譯：Two-Stream Convolutional Networks for Action Recognition in Videos
- 3. 《Two-Stream Convolutional Networks for Action Recognition in Videos》論文筆記
- 4. 視頻動作識別--Two-Stream Convolutional Networks for Action Recognition in Videos
- 5. Two-Stream Convolutional Networks for Action Recognition in Videos讀書筆記
- 6. 論文閱讀：Two-Stream Convolutional Networks for Action Recognition in Videos
- 7. Two-Stream Convolutional Networks for Action Recognition in Videos翻譯
- 8. Two-Stream Convolutional Networks for Action Recognition in Videos算法筆記
- 9. 譯：Two-Stream Convolutional Networks for Action Recognition in Videos.md
- 10. Two-Stream SR-CNNs for Action Recognition in Videos