【因果推断】（二）CV中的应用
文章目录
- 因果表征学习
- 因果图 (Causal Diagram)
- “后门准则”（backdoor criterion）和“前门准则”（frontdoor criterion）
- 后门调整
- Visual Commonsense R-CNN
- Causal Intervention for Weakly-Supervised Semantic Segmentation
- Causal Intervention and Parameter-Free Reasoning for Few-Shot SAR Target Recognition
- A Domain Generalization Network Exploiting Causal Representations and Non-Causal Representations for Three-Phase Converter Fault Diagnosis
- Causal Prototype-Inspired Contrast Adaptation for Unsupervised Domain Adaptive Semantic Segmentation of High-Resolution Remote Sensing Imagery
- Causality-Inspired Single-Source Domain Generalization for Medical Image Segmentation
- Generalizable Single-Source Cross-Modality Medical Image Segmentation via Invariant Causal Mechanisms
- Causality-inspired Unsupervised Domain Adaptation with Target Style Imitation for Medical Image Segmentation
- CPI-Parser: Integrating Causal Properties Into Multiple Human Parsing
- Revisiting Few-Shot Learning From a Causal Perspective
- Causal Meta-Transfer Learning for Cross-Domain Few-Shot Hyperspectral Image Classification
因果表征学习
因果图 (Causal Diagram)
如果整个 DAG 的结构已知且所有的变量都可观测，那么我们可以根据上面 do 算子的公式算出任意变量之间的因果作用。但是，在绝大多数的实际问题中，我们既不知道整个 DAG 的结构，也不能将所有的变量观测到。
“后门准则”（backdoor criterion）和“前门准则”（frontdoor criterion）
后门准则：Z可以同时影响or产生 X/Y，那么Z就相当于X/Y因果关系的后门（不影响X-> Y之间，影响两者），Z也是X/Y的混杂因子
前门准则：Z影响X-> Y的前门路径（直接影响X->Y）
后门调整
Z在X-> Y的后门路径上，那么一般会利用do算子进行干预，进行【后门调整】，可以看到这里从do(X) -> (X,z)
Visual Commonsense R-CNN
Causal Intervention for Weakly-Supervised Semantic Segmentation
Causal Intervention and Parameter-Free Reasoning for Few-Shot SAR Target Recognition
A Domain Generalization Network Exploiting Causal Representations and Non-Causal Representations for Three-Phase Converter Fault Diagnosis
Causal Prototype-Inspired Contrast Adaptation for Unsupervised Domain Adaptive Semantic Segmentation of High-Resolution Remote Sensing Imagery
Causality-Inspired Single-Source Domain Generalization for Medical Image Segmentation
Generalizable Single-Source Cross-Modality Medical Image Segmentation via Invariant Causal Mechanisms
Causality-inspired Unsupervised Domain Adaptation with Target Style Imitation for Medical Image Segmentation
CPI-Parser: Integrating Causal Properties Into Multiple Human Parsing
Revisiting Few-Shot Learning From a Causal Perspective
Causal Meta-Transfer Learning for Cross-Domain Few-Shot Hyperspectral Image Classification
