論文筆記：GSNN: The More You Know: Using Knowledge Graphs for Image Classification
Paper, CMU, CVPR 2017 人類有從世界中獲取信息並進行推理的能力，可以通過了解物體的特徵以及特徵之間的的關係來從少量的樣本中學習視覺概念。 通過結構化的先驗知識以知識圖譜的形式來優化image的分類：recognizing, recalling knowledge, reasoning。不再從整體上識別一個物體，而是識別部分特徵，通過推理的方式理解物體的類別 方法： propag
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相關文章
- 1. 《Multi-Label Zero-Shot Learning With Structured Knowledge Graphs》論文筆記
- 2. 論文筆記：Residual Attention Network for Image Classification
- 3. 論文筆記003-《Entity Alignment between Knowledge Graphs Using Attribute Embeddings》
- 4. 【論文筆記】SEEK: Segmented Embedding of Knowledge Graphs
- 5. 論文筆記《Active Convolution: Learning the Shape of Convolution for Image Classification》
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- 7. 【論文筆記】Rethinking Few-Shot Image Classification: A Good Embedding Is All You Need?
- 8. 論文筆記：Distilling the Knowledge
- 9. The 9 Deep Learning Papers You Need To Know About
- 10. 論文筆記008-《A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs》