vault backup: 2025-02-21 15:27:04

Paper/CLIP/Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models.md

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-# TODO
-- How to train encoder with the opposite modality frozen encoder?
-- How to generate high level prompt?
-- Why add the category-agnostic vector as global prompt?

Paper/CLIP/Open-Vocabulary Semantic Segmentation.md

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-## Introduction
-- Two-stage approach
-	- 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.
-		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 imperfect region proposal)
-	- Conclusion
-	  Pre-trained CLIP not performed well over masked images, we hypothesize that CLIP trained on natural image which are not cropped or noised by segmentation masks.
-
-## Vocabularies
-1. ground-truth masks: refer to the manually annotated masks or pixel-level labels that are used to define the correct segmentation of objects in an image. Each pixel in the ground-truth mask is assigned a specific class label corresponding to the object or region it belongs to.

Paper/FSL Image Classification/Context Attribute words for Prompt Tuning.md

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+---
+CreateAt: 2025-02-21
+ModleName: CoAPT
+Repo: https://github.com/LeeGun4488/CoAPT
+---