part-aligned系列論文：1707.Deep Representation Learning with Part Loss for Person ReID 論文閱讀筆記
Deep Representation Learning with Part Loss for Person ReID 本論文爲了更好的提升reid模型在未見過的行人圖像判別能力，正對現有大部分只有全局特徵表達（轉化爲分類，一般minimize the empirical classification risk即loss）且容易過擬合而不考慮parts 局部特徵表達（the representat
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相關文章
- 1. part-aligned系列論文：1707.Deeply-Learned Part-Aligned Representations for Person Re-Identification 論文筆記
- 2. part-aligned系列論文：1707.ICCV.Pedestrian Alignment Network for Large-scale Person Re-identification 論文
- 3. part-aligned系列論文：1711.Beyond Part Models- Person Retrieval with Refined Part Pooling 論文閱讀筆記
- 4. 1707.ICCV.Neural Person Search Machines檢測加識別 論文閱讀筆記
- 5. 《Learning Deep Representation for Face Alignment with Auxiliary Attributes》論文閱讀筆記
- 6. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
- 7. 1505.Deep Ranking for Person Re-identification via Joint Representation Learning論文筆記
- 8. Machine Learning & Deep Learning 論文閱讀筆記
- 9. 1604.Joint Detection and Identification Feature Learning for Person Search論文閱讀筆記
- 10. 1604.An Enhanced Deep Feature Representation for Person Re-identification 論文筆記