What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
Abstract 神经网络的研究重点从算法的研究过度到合适（suitable）且大量训练数据的创建。传统的计算机视觉任务通过人工标注的网络数据来获得训练集。对于光流和场景流问题，由于无法人为进入每个像素精确光流场的限制，所以通过人工标注数据集的方法不可行。此论文提倡使用合成数据集来训练神经网络，并以此实现光流以及场景流的计算。此论文利用不同的合成训练集来训练神经网络，并评估了不同合
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相关文章
- 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. 论文解读2-Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation
- 9. 人群计数：SFCN--Learning from Synthetic Data for Crowd Counting in the Wild
- 10. Game Development Theory 1：What makes a game ‘good’?