1703.In Defense of the Triplet Loss for Person Re-Identification 論文閱讀筆記
1703.In Defense of the Triplet Loss for Person Re-Identification-v2 本文爲Triplet 的改進方法進行end-to-end 的基於深度學習的度量學習，作者改進了tripletloss，分別採用現成的可參數遷移的resnet-50v2模型和基於resnet設計的從頭訓練的模型進行了實驗，在CUHK03，Market-1501和MA
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相關文章
- 1. In Defense of the Triplet Loss for Person Re-Identification
- 2. 【行人重識別】《In Defense of the Triplet Loss for Person Re-Identification》論文閱讀
- 3. 1705.IAN- The Individual Aggregation Network for Person Search 論文閱讀筆記
- 4. 【TripletTracking(ECCV2018)】論文閱讀 Triplet Loss in Siamese Network for Object Tracking
- 5. 1604.Joint Detection and Identification Feature Learning for Person Search論文閱讀筆記
- 6. In Defense of Grid Features for Visual Question Answering論文閱讀
- 7. 【論文閱讀筆記】Towards Accurate Multi-person Pose Estimation in the Wild
- 8. Bag of Tricks and A Strong Baseline for Deep Person Re-identification（論文閱讀筆記）
- 9. [論文筆記]Modeling Inter and Intra-Class Relations in the Triplet Loss for Zero-Shot Learning
- 10. 【論文閱讀筆記】Vision-Based Fallen Person Detection for the Elderly