《Joint End-to-end Learning of Global and Local Image Priors》图像上色论文解读
本篇paper的思路总体理解可归纳为：整体局部联合+双任务组合优化+支持任意输入尺寸+低端网络权值共享，使用了4个网络结构，分别预测图片所属的类别 和利用L通道预测ab通道值，网络从左到右经历过程为： [1]将原始L通道图分别经过尺寸缩减到112*112、缩半处理后传到上下权值共享的 低级特征网络进行特征提取，通过6层的CNN得到低阶特征信息。 [2]在下
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相关文章
- 1. Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image
- 2. 【论文笔记】Joint Unsupervised Learning of Deep Representations and Image Clusters
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- 4. 图像拼接--Construction and Refinement of Panoramic Mosaics with Global and Local Alignment
- 5. 1607.CVPR-Joint Learning of Single-image and Cross-image Representations for Person ReID 论文笔记
- 6. [CVPR 2020] D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
- 7. 论文阅读：Associative Embedding: End-to-End Learning for Joint Detection and Grouping
- 8. 自监督学习（四）Joint Unsupervised Learning of Deep Representations and Image Clusters
- 9. [论文解读]D2-Net: A Trainable CNN for Joint Description and Detection of Local Features
- 10. 1604.Joint Detection and Identification Feature Learning for Person Search论文阅读笔记