Add expected metrics and system architecture for open-world segmentation

[NishantSinghhhhh]

What this PR does / why we need it:

This PR updates the documentation under docs/proposals/algorithms/lifelong-learning/ to include
a clearer, structured and more complete description of the SAM + SSA based open-world segmentation and
annotation framework integrated into Ianvs.

The documentation now includes:

• Expanded Background explaining the need for open-world segmentation and lifelong learning
• Clear Goals and Expected Metrics (mIoU, AP, annotation efficiency, real-world performance)
• Updated Proposal section describing how SAM + SSA will be used for open-domain annotation
• Newly added High-Level Architecture section with final flow explanation + system diagram
  • Cloud–Edge feedback loop
  • SAM + SSA auto-segmentation
  • Label Studio based human-in-loop refinement
  • Continual learning retraining cycle
• Added JPEG architecture diagram → docs/proposals/algorithms/lifelong-learning/images/High_Level_Architecture_SAM.jpeg
• Reformatted and cleaned the documentation for readability and future additions

This brings the proposal documentation closer to a final structure suitable for review and
future development work in the lifelong-learning annotation module.

Fixes:
Fixes #92

Work Completed ✔

Section	Status
Background + Motivation	✔ Updated
Goals & Expected Metrics	✔ Added/Refined
Proposal	✔ Refined
High-Level Architecture	✔ Added + New diagram
System Architecture Overview	✔ Added
Design Details (SAM/SSA/Datasets)	✔ Rewritten for clarity

This PR improves readability, completeness, and structure of the document and takes
the SAM proposal closer to final integration under Ianvs.

[gemini-code-assist]

Summary of Changes

Hello @NishantSinghhhhh, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the documentation for the SAM + SSA based open-world segmentation and annotation framework integrated into Ianvs. It provides a more structured and complete description by detailing expected performance metrics, outlining the high-level system architecture with a new diagram, and clarifying the overall proposal. The update aims to prepare the proposal for review and subsequent development work in the lifelong-learning annotation module.

Highlights

• Expected Metrics Defined: Detailed expected metrics have been added for segmentation quality (mIoU, AP, Boundary Accuracy), annotation efficiency (reduction in manual workload, practical usability), and system performance (stable batch annotation, edge/cloud compatibility, scalability).
• High-Level Architecture Introduced: A new 'High-Level Architecture' section has been incorporated, providing a comprehensive overview of the system's ongoing loop for annotation and training feedback, including the cloud-edge feedback loop, SAM/SSA auto-segmentation, Label Studio-based human-in-loop refinement, and continual learning retraining cycle.
• New Architecture Diagram: A JPEG image illustrating the 'High_Level_Architecture_SAM' has been added to visually represent the system's design.
• Documentation Restructuring: The entire document has been reformatted and re-numbered to improve readability, structure, and logical flow, accommodating the newly added sections and ensuring clarity for future additions.

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[gemini-code-assist]

Code Review

This pull request significantly improves the documentation for the open-world segmentation proposal by adding expected metrics and a high-level system architecture. The changes add structure and clarity. My feedback focuses on improving the professionalism and clarity of the language used in the new sections to align with the standards of technical documentation.

[RONAK-AI647]

@NishantSinghhhhh , to solve the commit issues and CI issues:

1. Sign off the DCO
2. reframe the commit , you commit should not include special characters like @ & _ , try using simple and to the goal points.

[NishantSinghhhhh]

@MooreZheng @hsj576 , I have completed my work on this Pull request, can you please review it and tell me if any more changes are required in here

[kubeedge-bot]

Keywords which can automatically close issues and at(@) or hashtag(#) mentions are not allowed in commit messages.

The list of commits with invalid commit messages:

• 0183efb Update docs Toward Automatic Image Annotation Algorithm with Large Foundation Model Based on Edge-cloud Collaborative Lifelong Learning to Reproduce Open World Object Segmentation

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[APPROVALNOTIFIER] This PR is NOT APPROVED

This pull-request has been approved by: NishantSinghhhhh
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[NishantSinghhhhh]

@hsj576 @MooreZheng , I have restored the previous proposal and added the new file with updated content and image