vault backup: 2023-12-19 20:16:21

Paper/Open-Vocabulary Semantic Segmentation.md

@@ -10,6 +10,10 @@
 		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.
+		4. Use MaskFormer(a mask proposal generator trained on COCO) as an region proposal generator.
+		5. Select the region proposals with highest overlap with ground-truth masks.
+		6. Assign the object label to this region.
+		7. This model reach mIoU of 66.5%(despite)
 
 
 ## Vocabularies