譯：Two-Stream Convolutional Networks for Action Recognition in Videos.md
摘要：我們研究了用於訓練視頻中行爲識別的深度卷積網絡架構。這個挑戰是捕捉靜態幀中的外觀和連續幀間的運動的互補信息。我們也旨在推廣這個在數據驅動的學習框架中表現得最好的手工特徵。 本文一共做出了3個貢獻： 首先，本文提出了一個two-stream卷積網絡架構，這個架構由時間和空間網絡構成。 然後，我們驗證了，儘管是有限的訓練數據集，在多幀密集光流上訓練的卷積網絡仍然能夠表現出很好的性能。 最後，我們
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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. 譯：3D Convolutional Neural Networks for Human Action Recognition
- 4. Two-Stream Convolutional Networks for Action Recognition in Videos翻譯
- 5. 論文：3D Convolutional Neural Networks for Human Action Recognition
- 6. 3D Convolutional Neural Networks for Human Action Recognition
- 7. Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
- 8. 3DCNN:3D Convolutional Neural Networks for Human Action Recognition
- 9. Two-Stream Convolutional Networks for Action Recognition in Videos算法筆記
- 10. 《Two-Stream Convolutional Networks for Action Recognition in Videos》論文筆記