《Learning Semantic Concepts and Order for Image and Sentence Matching》
Learning Semantic Concepts and Order for Image and Sentence Matching CVPR 2018 一、Motivation 目前该领域主要问题之一是像素级别的图片描述缺少高层次的语义信息，之前的做法都是提取一个全局的CNN特征向量。这样一些主要的信息就会占据主导地位，背景一些的就会被忽略。这篇文章提出了语义增强图片及语句匹配模型。 二、M
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- 1. Learning Semantic Concepts and Order for Image and Sentence Matching笔记
- 2. 读论文，多区块处理：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
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- 6. Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information阅读笔记
- 7. Locally Adaptive Color Correction for Underwater Image Dehazing and Matching
- 8. 【论文总结】weakly- and semi-supervised learning of a DCNN for semantic Image Segmentation
- 9. [IDW-CNN]Learning Object Interactions and Descriptions for Semantic Image Segmentation
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相关文章
- 1. Learning Semantic Concepts and Order for Image and Sentence Matching笔记
- 2. 读论文，多区块处理：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阅读笔记
- 7. Locally Adaptive Color Correction for Underwater Image Dehazing and Matching
- 8. 【论文总结】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. 语义分割--Learning Object Interactions and Descriptions for Semantic Image Segmentation