HyNet: Local Descriptor with Hybrid Similarity Measure and Triplet Loss(2020)(十二)
本文要解決的問題： 從公式推導 descriptor如何訓練更加完美，僅僅解決descriptor的問題，detector使用sift或者別的網絡得到。 1.梯度下降分析 1.1 首先是梯度方向分析 上述1中的決定梯度方向是分別對x和y求偏導數可以得到上述結果，我們假設： 梯度的方向是∆ = ∆∥ + ∆⊥，其中∆∥表示和特徵 向量x或者y平行的向量， ∆
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相關文章
- 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. 【論文翻譯】A Novel Method for Fast and Accurate Similarity Measure in Time Series Field
- 6. Triplet Loss入門
- 7. Improved Embeddings with Easy Positive Triplet Mining
- 8. GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval 論文閱讀筆記
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- 10. [2015 Springer] Local Image Descriptor: Modern Approaches——3 Intensity Order-Based Local Descriptors