【ReID】【Skimming】Unsupervised Cross-Dataset Transfer Learning for Person Re-identification
【ReID】【Skimming】Unsupervised Cross-Dataset Transfer Learning for Person Re-identification 痛點 模型 實驗 寫作 參考文獻 現有的大多數工作都是基於監督學習，而監督學習需要大量標註數據，嚴重限制了它的可拓展性。因而可遷移跨數據集的無監督模型被提出，且數據完全不需要標註。本文開發了一種基於非對稱多任務字典學習的
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- 1. 【ReID】【Skimming】Unity Style Transfer for Person Re-Identification
- 2. 【ReID】Hierarchical and Efficient Learning for Person Re-Identification
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