[CVPR 2018]Discriminative Learning of Latent Features for Zero-Shot Recognition
文章目录 背景 贡献 模型 The Image Feature Network（FNet） The Zoom Network（ZNet） The Embedding Network(ENet) Prediction 实验 背景 作者认为当前(2018年前)zero-shot研究的核心主要是在视觉空间和语意空间中学习合适的映射关系，而忽视了学习ZSL问题中 有判别力的表示。几乎所有的模型都有着相似的
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相关文章
- 1. [CVPR 2018笔记] Discriminative Learning of Latent Features for Zero-Shot Recognition
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- 4. Latent Embeddings for Collective Activity Recognition
- 5. CVPR2017: Learning Deep Context-aware Features over Body and Latent Parts for
- 6. [CVPR 2020] D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
- 7. Deep Clustering for Unspervised Learning of Visual Features
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- 10. 行人属性“Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Loca”