diff --git a/Paper/Open-Vocabulary Semantic Segmentation.md b/Paper/Open-Vocabulary Semantic Segmentation.md
index 0d9443d..4bd3882 100644
--- a/Paper/Open-Vocabulary Semantic Segmentation.md	
+++ b/Paper/Open-Vocabulary Semantic Segmentation.md	
@@ -1,7 +1,15 @@
 ## Introduction
 - Two-stage approach
-	1. Generate class-agnostic mask proposal
-	2. 
+	- Method
+		1. Generate class-agnostic mask proposal.
+		2. Leverage pre-trained CLIP to perform open-vocabulary classification.
+	- Assumption
+		1. The model can generate classagnostic mask proposals.
+		2. Pre-trained CLIP can transfer its classification performance to masked image proposals.
+	- Examination
+		1. Using ground-truth masks as region proposal.
+		2. Feed masked images to a pre-trained CLIP for classification.
+		3. Get mIoU of 20.1% on the ADE20K-150 dataset.
 
 
 ## Vocabularies