Normalized and Geometry-Aware Self-Attention Network for Image Captioning
重點在自注意力機制的image captioning方法上。 現有的Self-Attention方法作者認爲存在兩個問題： 一個是：Internal Covariate Shift 我的理解就是輸入分佈不一樣 解決辦法就是Normalization。 原來的Transformer當中也是有Normalization的，但是作者認爲原來的做法不夠好： 翻譯過來，就是要把norm放到自注意力模塊裏面
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相關文章
- 1. Reflective Decoding Network for Image Captioning論文閱讀
- 2. Self-critical Sequence Training for Image Captioning
- 3. 《Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering》
- 4. Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
- 5. (Paper Reading)Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
- 6. CVPR 2018 Oral：Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
- 7. Pose-Normalized Image Generation for Person Re-identification (note)
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- 9. Ncut算法（Normalized cuts and image segmentation）
- 10. PEPSI++: Fast and Lightweight Network for Image Inpainting | 簡記