[NS][Lab_Seminar_250106]SAM-Aware Graph Prompt Reasoning Network fo...

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SAM-Aware Graph Prompt
Reasoning Network for Cross-
Domain Few-Shot
Segmentation
Tien-Bach-Thanh Do
Network Science Lab
De...

2
Introduction
Figure 1: Comparison of existing visual prompting methods and ours. (a) Existing methods typically input ra...

3
Related Work
Cross-Domain Few-Shot Segmentation
● Lei (2022) establish widely used benchmark and design pyramid transfor...

4
Related Work
Prompt Learning
● Manual prompt design requires specialized knowledge
● Jia, Wu (2022) optimize prompt thro...

5
Model
Figure 2: Overall architecture of our method. In the training or fine-tuning phase, support and query features alo...

6
Model
Task definition
● Source domain (Xs, Ys) and target domain (Xt, Yt), where X is data distribution, Y is label spac...

7
Model
Method Overview
● Consist of 3 main modules:
○ SAM-aware prompt initialization (SPI)
○ Graph prompt reasoning (GPR...

8
Model
SAM-aware Prompt Initialization
● Limitation: visual prompt initialized randomly and do not contain prior informat...

9
Model
Graph Prompt Reasoning
● Problem: SAM divides single object into many sub-regions
○ Prompt belonging to same categ...

10
Model
Self-support Prototype Matching
● To restore spatial information of visual prompts, apply RMAP
● Features of same...

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Model
Self-support Prototype Matching
● SSP
Figure 4: Flowchart of SSP. SSFP refers to the self-support foreground prot...

12
Model
Self-support Prototype Matching
● SSP is prototype-based FSS method, idea is to use query prototypes to match que...

13
Model
Adaptive Point Selection
● Problem: After have initial prediction , accuracy is suboptimal
● Solution: refine usi...

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Model
Adaptive Point Selection
● Negative prompts N derived from background mask in
● Input Z and N into SAM with query...

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Model
Loss Function
● Binary cross entropy function

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Experiments
Experimental Settings
● Datasets:
○ Train: PASCAL VOC augmented with SDS
○ Fine-tune + test:
■ Deepglobe
■ ...

17
Experiments
Comparison results
Table 1: Comparison of model performance in 1-shot and 5-shot settings with and without ...

18
Experiments
Ablation Study
Table 2: Effects of different
modules.
Table 3: Effects of different α. Table 4: Effects of ...

19
Experiments
Ablation Study
Table 8: Performance of GPRN trained on the PASCAL VOC with and without the fine-tuning phas...

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Experiments
Visualization
Figure 3: Qualitative analysis results: Iq and M q represent the original query image and its...

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Conclusion
● Propose SAM-aware graph prompt network to tackle CD-FSS task
● Transform masks generated by SAM into sever...

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