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pages/CSE559A/CSE559A_L14.md

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+# CSE559A Lecture 14
+
+## Neural Network Training
+
+## Object Detection
+
+AP (Average Precision)
+
+### Benchmarks
+
+#### PASCAL VOC Challenge
+
+20 Challenge classes.
+
+CNN increases the accuracy of object detection.
+
+#### COCO dataset
+
+Common objects in context.
+
+Semantic segmentation. Every pixel is classified to tags.
+
+Instance segmentation. Every pixel is classified and grouped into instances.
+
+### Object detection: outline
+
+Proposal generation
+
+Object recognition
+
+#### R-CNN
+
+Proposal generation
+
+Use CNN to extract features from proposals.
+
+with SVM to classify proposals.
+
+Use selective search to generate proposals.
+
+Use AlexNet finetuned on PASCAL VOC to extract features.
+
+Pros:
+
+- Much more accurate than previous approaches
+- Andy deep architecture can immediately be "plugged in"
+
+Cons:
+
+- Not a single end-to-end trainable system
+  - Fine-tune network with softmax classifier (log loss)
+  - Train post-hoc linear SVMs (hinge loss)
+  - Train post-hoc bounding box regressors (least squares)
+- Training is slow 2000CNN passes for each image
+- Inference (detection) was slow
+
+#### Fast R-CNN
+
+Proposal generation
+
+Use CNN to extract features from proposals.
+
+##### ROI pooling and ROI alignment
+
+ROI pooling:
+
+- Pooling is applied to the feature map.
+- Pooling is applied to the proposal.
+
+ROI alignment:
+
+- Align the proposal to the feature map.
+- Align the proposal to the feature map.
+
+Use bounding box regression to refine the proposal.
+
+

pages/CSE559A/_meta.js

@@ -16,4 +16,5 @@ export default {
     CSE559A_L11: "Computer Vision (Lecture 11)",
     CSE559A_L12: "Computer Vision (Lecture 12)",
     CSE559A_L13: "Computer Vision (Lecture 13)",
+    CSE559A_L14: "Computer Vision (Lecture 14)",
 }