1604.An Enhanced Deep Feature Representation for Person Re-identification 論文筆記
這篇論文發表於IEEE Winter Conference on Applications of Computer Vision (WACV’2016) 論文類型：嚴格來說，該論文屬於基於融合特徵的度量學習方法，側重點在深度特徵和傳統特徵的融合，更魯棒的表達。 1.作者的idea： 總結前人的研究成果，對傳統特徵表達做一次進一步的整合，再結合cnn深度特徵提取，最後做一個特徵融合，而融合的過程並不
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相關文章
- 1. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
- 2. 1604.Joint Detection and Identification Feature Learning for Person Search論文閱讀筆記
- 3. 1505.Deep Ranking for Person Re-identification via Joint Representation Learning論文筆記
- 4. part-aligned系列論文：1707.Deeply-Learned Part-Aligned Representations for Person Re-Identification 論文筆記
- 5. Re-ID: Learning Deep Feature Representation with Domain Guided Dropout for Person Re-identifiation
- 6. 1604. Learning deep feature representations with domain guided dropout for re-id論文閱讀筆記
- 7. 【論文筆記】Text-Enhanced Representation Learning for Knowledge Graph
- 8. 1610.Deep Learning Prototype Domains for Person Re-Identification-CUHK-SYSU-PRW 論文筆記
- 9. SentiLR:Linguistic Knowledge Enhanced Language Representation for Sentiment Analysis 論文閱讀筆記
- 10. 論文筆記 Pose-driven Deep Convolutional Model for Person Re-identification_tianqi_2017_ICCV