What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
Abstract 神經網絡的研究重點從算法的研究過度到合適（suitable）且大量訓練數據的創建。傳統的計算機視覺任務通過人工標註的網絡數據來獲得訓練集。對於光流和場景流問題，由於無法人爲進入每個像素精確光流場的限制，所以通過人工標註數據集的方法不可行。此論文提倡使用合成數據集來訓練神經網絡，並以此實現光流以及場景流的計算。此論文利用不同的合成訓練集來訓練神經網絡，並評估了不同合
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相關文章
- 1. What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
- 2. What are some good books/papers for learning deep learning?
- 3. ProFlow: Learning to Predict Optical Flow
- 4. Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
- 5. LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
- 6. FlowNet: Learning Optical Flow with Convolutional Networks
- 7. FlowNet 2.0 Evolution of Optical Flow Estimation with Deep Networks
- 8. 論文解讀2-Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation
- 9. 人羣計數：SFCN--Learning from Synthetic Data for Crowd Counting in the Wild
- 10. Game Development Theory 1：What makes a game ‘good’?