Deep Image Retrieval: Learning global representations for image search
Deep Image Retrieval: Learning global representations for image search 文中贡献 学习特征权重，构建区域特征。 2. 使用区域推荐网络，学习哪个区域需要细化形成池化构建成全局表达 文中使用三流孪生网络，优化R-MAC向量权重。通过使用triplet ranking loss。 第二个贡献是学习R-MAC pooling机制。
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相关文章
- 1. Deep Image Retrieval: Learning GlobalRepresentations for Image Search
- 2. End-to-End Learning of Deep Visual Representations for Image Retrieval
- 3. Deep Supervised Hashing for Fast Image Retrieval
- 4. Deep Residual Learning for Image Recognition
- 5. Binary Code for Image Search
- 6. Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval
- 7. Deep Hash in Large Scale Image Retrieval
- 8. Deep Learning for Information Retrieval
- 9. Deep Residual Learning For Image Recognition
- 10. Hierarchical deep semantic hashing for fast image retrieval