HyNet: Local Descriptor with Hybrid Similarity Measure and Triplet Loss(2020)(十二)
本文要解决的问题： 从公式推导 descriptor如何训练更加完美，仅仅解决descriptor的问题，detector使用sift或者别的网络得到。 1.梯度下降分析 1.1 首先是梯度方向分析 上述1中的决定梯度方向是分别对x和y求偏导数可以得到上述结果，我们假设： 梯度的方向是∆ = ∆∥ + ∆⊥，其中∆∥表示和特征 向量x或者y平行的向量， ∆
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相关文章
- 1. Hybrid Qt applications with PySide and Django
- 2. Bit-Scalable Deep Hashing with Regularized Similarity Learning for Image Retrieval and Person Re-ide
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- 4. Video Input and Output1（Video Input with OpenCV and similarity measurement）
- 5. 【论文翻译】A Novel Method for Fast and Accurate Similarity Measure in Time Series Field
- 6. Triplet Loss入门
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- 10. [2015 Springer] Local Image Descriptor: Modern Approaches——3 Intensity Order-Based Local Descriptors