Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Conv
Proposed Methodology Heatmap Regression 將預測AU intensity vector的問題轉換爲預測multiple AU heatmaps Fig.2給出了每一個AU的central location Q：每一個點都應該由68個landmarks通過某些規則計算得到的吧，文中沒有仔細說明 AU數量爲 N N N，對於第 i i i個AU，其location
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相關文章
- 1. CVPR: Bilateral Ordinal Relevance Multi-instance Regression for Facial Action Unit Intensity
- 2. CVPR：Weakly-supervised Deep Convolutional Neural Network Learning for Facial Action Intensity Estima
- 3. Real-time Facial Animation with Image-based Dynamic Avatars
- 4. Self-supervised Representation Learning from Videos for Facial Action Unit Detection(CVPR 2019)
- 5. Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base（筆記）
- 6. 【筆記】Action Unit Detection with Region Adaptation Multi-labeling Learning and Optimal Temporal Fusing
- 7. CVPR：Facial Expression Intensity Estimation Using Ordinal Information
- 8. 《Learning Dynamic Routing for Semantic Segmentation》
- 9. FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORK
- 10. ICCV 2017論文分析（文本分析）標題詞頻分析 這算不算大數據 第一步：數據清洗（刪除做者和無用的頁碼）