《Learning Semantic Concepts and Order for Image and Sentence Matching》
Learning Semantic Concepts and Order for Image and Sentence Matching CVPR 2018 一、Motivation 目前該領域主要問題之一是像素級別的圖片描述缺少高層次的語義信息，之前的做法都是提取一個全局的CNN特徵向量。這樣一些主要的信息就會佔據主導地位，背景一些的就會被忽略。這篇文章提出了語義增強圖片及語句匹配模型。 二、M
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相關文章
- 1. Learning Semantic Concepts and Order for Image and Sentence Matching筆記
- 2. 讀論文，多區塊處理：Learning Semantic Concepts and Order forImage and Sentence Matching
- 3. Image-Text Matching and VQA
- 4. [AAAI2019] DRr-Net:Dynamic Re-read Netwok for Sentence Semantic Matching
- 5. MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching
- 6. Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information閱讀筆記
- 7. Locally Adaptive Color Correction for Underwater Image Dehazing and Matching
- 8. 【論文總結】weakly- and semi-supervised learning of a DCNN for semantic Image Segmentation
- 9. [IDW-CNN]Learning Object Interactions and Descriptions for Semantic Image Segmentation
- 10. 語義分割--Learning Object Interactions and Descriptions for Semantic Image Segmentation