2019 ArXiv之ReID：Hetero-Center Loss for Cross-Modality Person Re-Identification

时间 2021-01-07

标签多模态 ReID 人工智能深度学习神经网络繁體版

原文原文链接

Hetero-Center Loss for Cross-Modality Person Re-Identification 当前的问题及概述：目前所有的框架都在解决跨模态差异问题，很少有研究探讨改进类内跨模态相似性。本文提出了一个新的损失函数，称为异中心损失(HC损失)，以减少类内交叉模态的变化。具体来说，HC损失可以通过约束两个异质模态之间的类内中心距离来监督网络学习的跨模态不变信息。在交

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1. 2019 ArXiv之ReID：Attend to the Difference: Cross-Modality Person Re-identification via Contrastive
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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 论文笔记
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

>>更多相关文章<<