update

pages/CSE559A/CSE559A_L14.md

@@ -72,6 +72,4 @@ ROI alignment:
 - Align the proposal to the feature map.
 - Align the proposal to the feature map.
 
-Use bounding box regression to refine the proposal.
-
-
+Use bounding box regression to refine the proposal.

pages/CSE559A/CSE559A_L15.md

@@ -0,0 +1,31 @@
+# CSE559A Lecture 15
+
+## Continue on object detection
+
+### Two strategies for object detection
+
+#### R-CNN: Region proposals + CNN features
+
+![R-CNN](https://notenextra.trance-0.com/CSE559A/R-CNN.png)
+
+#### Fast R-CNN: CNN features + RoI pooling
+
+![Fast R-CNN](https://notenextra.trance-0.com/CSE559A/Fast-R-CNN.png)
+
+Use bilinear interpolation to get the features of the proposal.
+
+#### Region of interest pooling
+
+![RoI pooling](https://notenextra.trance-0.com/CSE559A/RoI-pooling.png)
+
+Use backpropagation to get the gradient of the proposal.
+
+### New materials
+
+#### Faster R-CNN
+
+Use one CNN to generate region proposals. And use another CNN to classify the proposals.
+
+
+
+

pages/CSE559A/_meta.js

@@ -17,4 +17,5 @@ export default {
     CSE559A_L12: "Computer Vision (Lecture 12)",
     CSE559A_L13: "Computer Vision (Lecture 13)",
     CSE559A_L14: "Computer Vision (Lecture 14)",
+    CSE559A_L15: "Computer Vision (Lecture 15)",
 }