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(Paper Reading)Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
時間 2020-12-30
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Introduction Within our approach, the bottom-up mechanism (based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weight
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Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
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相關文章
1.
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
2.
《Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering》
3.
CVPR 2018 Oral:Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
4.
論文筆記:Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
5.
論文-《Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering 》重點翻譯+擴展
6.
論文筆記:Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answer
7.
視覺場景理解論文閱讀筆記:Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
8.
論文解讀:Hierarchical Question-Image Co-Attention for Visual Question Answering
9.
Normalized and Geometry-Aware Self-Attention Network for Image Captioning
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