2019 ArXiv之ReID：Hetero-Center Loss for Cross-Modality Person Re-Identification
Hetero-Center Loss for Cross-Modality Person Re-Identification 當前的問題及概述： 目前所有的框架都在解決跨模態差異問題，很少有研究探討改進類內跨模態相似性。 本文提出了一個新的損失函數，稱爲異中心損失(HC損失)，以減少類內交叉模態的變化。具體來說，HC損失可以通過約束兩個異質模態之間的類內中心距離來監督網絡學習的跨模態不變信息。在交
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相關文章
- 1. 2019 ArXiv之ReID：Attend to the Difference: Cross-Modality Person Re-identification via Contrastive
- 2. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
- 3. In Defense of the Triplet Loss for Person Re-Identification
- 4. LG Display posts steep loss for 2019
- 5. 2019 IET之ReID：HPILN: a feature learning framework for cross-modality person re-identification
- 6. Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification
- 7. 2019 CVPR之ReID:Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Id
- 8. 2019 TCSVT之ReID：SDL: Spectrum-Disentangled Representation Learning for Visible-Infrared Person Re-id
- 9. 2019 TIP之ReID：Learning Modality-Specific Representations for Visible-Infrared Person Re-Identificati
- 10. 2019 AAAI之ReID：HSME: Hypersphere Manifold Embedding for Visible Thermal Person Re-Identificatio