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# System prepended metadata

title: Foundation Model Assisted Weakly Supervised Semantic Segmentation

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type: slide
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### Foundation Model Assisted Weakly Supervised Semantic Segmentation


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#### Methods

- **CLIP**: Used with frozen weights for image classification and seed segmentation tasks.
- **SAM**: Provides class-agnostic object masks, improving the precision of segmentation seeds.

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#### Framework Overview

![image](https://hackmd.io/_uploads/H1lXidXL-A.png)


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#### SAM-based seeding module

- Quasi-superpixel generation
    - lower threshold $t_{m,whole}$ to retrieve more masks at the *whole* level
    - prioritze the selection of the *whole* level
    - apply filter
    
- Quasi-superpixel classification

- seed map generation

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#### Quasi-superpixel generartion

![image](https://hackmd.io/_uploads/B1JtFm8WC.png)

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- Quasi-superpixel classification
foerground:    ![image](https://hackmd.io/_uploads/SJWK5XUW0.png)
background:    ![image](https://hackmd.io/_uploads/SJu95XU-A.png)
semantic class:    ![image](https://hackmd.io/_uploads/rkci5XIZ0.png)
    
- seed map generation

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- Experimental result

![image](https://hackmd.io/_uploads/S1XuDzL-R.png)


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