极简笔记 Unsupervised Discovery of Object Landmarks as Structural Representations
Unsupervised Discovery of Object Landmarks as Structural Representations 本文的核心是提出一种无监督的方法检测物体的关键点。这个具有语义性质的关键点不是人为定义的而是自动学得的。 文章主要思路是利用autoencoder模型，在中间部分添加各种loss约束引导检测的关键点heatmap的生成。主要架构见上图，encoder部分
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相关文章
- 1. 【论文笔记】Unsupervised Discovery of Object Landmarks as Structural Representations
- 2. CVPR2018论文解读 Unsupervised Discovery of Object Landmarks as Structural Representations
- 3. ECCV2016【论文笔记】Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
- 4. 【论文笔记】Joint Unsupervised Learning of Deep Representations and Image Clusters
- 5. 【论文笔记】Unsupervised Learning of Video Representations using LSTMs
- 6. 极简笔记 CornerNet: Detecting Objects as Paired Keypoints
- 7. 极简笔记 A Survey on Transfer Learning
- 8. 论文笔记：Object as Points
- 9. 【论文笔记】Unsupervised Domain-Specific Deblurring via Disentangled Representations
- 10. [MICCAI2019]Unsupervised Domain Adaptation via Disentangled Representations