[CVPR 2018]Discriminative Learning of Latent Features for Zero-Shot Recognition
文章目錄 背景 貢獻 模型 The Image Feature Network（FNet） The Zoom Network（ZNet） The Embedding Network(ENet) Prediction 實驗 背景 作者認爲當前(2018年前)zero-shot研究的核心主要是在視覺空間和語意空間中學習合適的映射關係，而忽視了學習ZSL問題中 有判別力的表示。幾乎所有的模型都有着相似的
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相關文章
- 1. [CVPR 2018筆記] Discriminative Learning of Latent Features for Zero-Shot Recognition
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- 6. [CVPR 2020] D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
- 7. Deep Clustering for Unspervised Learning of Visual Features
- 8. Official Program for CVPR 2015
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- 10. 行人屬性「Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Loca」