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Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
時間 2021-05-23
標籤
自然語言處理
機器學習
數據挖掘
深度學習
简体版
原文
原文鏈接
原文鏈接:link 摘要 自頂向下的注意力機制在image caption和VQA中被廣泛地使用。本文提出的想法是結合top-down和bottom-up注意力機制。基於faster RCNN的bottom-up方法給出圖像區域的特徵向量,top-down注意力給出特徵的權重。 introduction 在人類的視覺系統中,注意力可以被由當前任務決定的自上而下的信號(例如尋找某物)集中起來,也可以
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論文-《Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering 》重點翻譯+擴展
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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.
(Paper Reading)Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
4.
CVPR 2018 Oral:Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
5.
論文筆記:Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
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-《Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering 》重點翻譯+擴展
9.
論文解讀:Hierarchical Question-Image Co-Attention for Visual Question Answering
10.
Normalized and Geometry-Aware Self-Attention Network for Image Captioning
>>更多相關文章<<