極簡筆記 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種無監督的方法檢測物體的關鍵點。這個具有語義性質的關鍵點不是人爲定義的而是自動學得的。 文章主要思路是利用autoencoder模型，在中間部分添加各種loss約束引導檢測的關鍵點heatmap的生成。主要架構見上圖，encoder部分
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相關文章
- 1. 【論文筆記】Unsupervised Discovery of Object Landmarks as Structural Representations
- 2. CVPR2018論文解讀 Unsupervised Discovery of Object Landmarks as Structural Representations
- 3. ECCV2016【論文筆記】Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
- 4. 【論文筆記】Joint Unsupervised Learning of Deep Representations and Image Clusters
- 5. 【論文筆記】Unsupervised Learning of Video Representations using LSTMs
- 6. 極簡筆記 CornerNet: Detecting Objects as Paired Keypoints
- 7. 極簡筆記 A Survey on Transfer Learning
- 8. 論文筆記：Object as Points
- 9. 【論文筆記】Unsupervised Domain-Specific Deblurring via Disentangled Representations
- 10. [MICCAI2019]Unsupervised Domain Adaptation via Disentangled Representations