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Normalized and Geometry-Aware Self-Attention Network for Image Captioning
時間 2020-12-30
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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)
8.
論文筆記:Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
9.
Ncut算法(Normalized cuts and image segmentation)
10.
PEPSI++: Fast and Lightweight Network for Image Inpainting | 簡記
>>更多相關文章<<