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What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
時間 2021-07-14
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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’?
>>更多相關文章<<