《Deep Transfer Learning for Person Re-identification》-閱讀筆記
行人重識別是一種對不同個體跨攝像頭的細粒度檢索任務，由於任務自身的特性，數據的標註代價遠大於其他常見的視覺任務，例如圖片分類（ImageNet）或者一些人臉匹配任務。目前最大的行人重識別數據集也僅僅只有數千人，十萬個左右的bounding box，這對於神經網絡的訓練來說，並不是一個充足的大小，因此，如果能將在一個或者若干個數據集上學習到的模型進行一些簡單的調節就可以應用到新的場景下降非常具有
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相關文章
- 1. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
- 2. 論文閱讀筆記：Transfer Learning for Deep Sentiment Analysis
- 3. Deep Residual Learning for Image Recognition閱讀筆記
- 4. Deep Reinforcement Learning for Dialogue Generation閱讀筆記
- 5. Machine Learning & Deep Learning 論文閱讀筆記
- 6. 1604.Joint Detection and Identification Feature Learning for Person Search論文閱讀筆記
- 7. Cross-view Asymmetric Metric Learning for Unsupervised Person Re-identification閱讀筆記
- 8. Feature Transfer Learning for Deep Face Recognition with Long-Tail Data 論文閱讀筆記
- 9. Deep Spatial Feature Reconstruction for Partial Person Re-identification: Freestyle Approach 閱讀筆記
- 10. 【行人識別】Deep Transfer Learning for Person Re-identification