Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Conv
Proposed Methodology Heatmap Regression 将预测AU intensity vector的问题转换为预测multiple AU heatmaps Fig.2给出了每一个AU的central location Q：每一个点都应该由68个landmarks通过某些规则计算得到的吧，文中没有仔细说明 AU数量为 N N N，对于第 i i i个AU，其location
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相关文章
- 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（笔记）
- 6. 【笔记】Action Unit Detection with Region Adaptation Multi-labeling Learning and Optimal Temporal Fusing
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- 8. 《Learning Dynamic Routing for Semantic Segmentation》
- 9. FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORK
- 10. ICCV 2017论文分析（文本分析）标题词频分析 这算不算大数据 第一步：数据清洗（删除做者和无用的页码）