《Context and Attribute Grounded Dense Captioning》筆記
CVPR 2019 《Context and Attribute Grounded Dense Captioning》這篇文章設計了一個端到端基於文本和屬性的描述架構，由上下文視覺挖掘模塊和基於多層屬性的描述生成模塊兩部分組成，同時還結合了來自分層語言的輔助監督，以增強學習的描述的獨特性。具體架構如下圖所示： 第一部分，上下文特徵提取器（CFE）。先用Faster RCNN學習輸入圖像的視覺特徵，
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相關文章
- 1. Dense-Captioning Events in Videos
- 2. DenseCap：Fully Convolutional Localization Networks for Dense Captioning
- 3. 論文筆記：Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
- 4. 論文筆記：Skeleton Key_Image Captioning by Skeleton-attribute Decomposition
- 5. 論文筆記：Image Captioning with Semantic Attention
- 6. Spatio-Temporal Dynamics and Semantic Attribute Enriched Visual Encoding for Video Captioning
- 7. 論文筆記：Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answer
- 8. 論文筆記：Show and Tell Lessons learned from the 2015 MSCOCO Image Captioning Challenge
- 9. 論文筆記：Areas of Attention for Image Captioning
- 10. 行人屬性「Attribute Recognition by Joint Recurrent Learning of Context and Correlation」