【2016_ECCV】Embedding Deep Metric for Person Re-identification: A Study Against Large Variations
行人數據的特徵空間分佈在highly-curved manifold，很難用geodesic距離比較2個樣本。manifold學習方法常採用局部範圍內的歐式距離結合樣本間的圖形關係來近似geodesic距離。文章主要通過moderate positive mining和對馬氏距離的權重限制來提高速度和性能。 Moderate positive mining 數據有很大的類內差異 數據在特徵空間中分
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相關文章
- 1. SphereReID: Deep Hypersphere Manifold Embedding for Person Re-Identification (note)
- 2. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
- 3. Re-ID: Learning Deep Feature Representation with Domain Guided Dropout for Person Re-identifiation
- 4. part-aligned系列論文：1707.ICCV.Pedestrian Alignment Network for Large-scale Person Re-identification 論文
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- 6. Stepwise Metric Promotion for Unsupervised Video Person Re-identification
- 7. DIVERSITY REGULARIZED METRIC LEARNING FOR PERSON RE-IDENTIFICATION（ICIP2016）
- 8. 【Person Re-id】A Discriminatively Learned CNN Embedding for Person Re-identification
- 9. Norm-Aware Embedding for Efficient Person Search
- 10. 論文筆記《Deep Cosine Metric Learning for Person Re-Identification》