RRepoGEO

REPOGEO REPORT · LITE

JIA-Lab-research/LISA

Default branch main · commit 3cb2d430 · scanned 5/28/2026, 2:48:15 PM

GitHub: 2,643 stars · 205 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface JIA-Lab-research/LISA, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Add a concise introductory paragraph to the README

    Why:

    CURRENT
    The README currently jumps from the title to navigation links and a table without an introductory paragraph.
    COPY-PASTE FIX
    LISA (Large Language Instructed Segmentation Assistant) is a novel multi-modal AI system that performs reasoning-based image segmentation by leveraging large language models to interpret complex instructions. This project introduces a new paradigm for visual understanding, moving beyond simple object detection to enable sophisticated, instruction-guided segmentation.
  • highhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://openxlab.org.cn/apps/detail/openxlab-app/LISA
  • mediumtopics#3
    Expand repository topics for better categorization

    Why:

    CURRENT
    large-language-model, llm, multi-modal, segmentation
    COPY-PASTE FIX
    large-language-model, llm, multi-modal, segmentation, reasoning-segmentation, instruction-following, vision-language-model

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface JIA-Lab-research/LISA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Grounding DINO
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Grounding DINO · recommended 2×
  2. Segment Anything Model (SAM) · recommended 2×
  3. OWL-ViT · recommended 2×
  4. CLIPSeg · recommended 2×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    How can I perform image segmentation using a large language model?
    you: not recommended
    AI recommended (in order):
    1. Grounding DINO
    2. Segment Anything Model (SAM)
    3. Hugging Face Transformers
    4. segment-anything library
    5. OWL-ViT
    6. CLIPSeg
    7. CLIP
    8. Mask2Former
    9. OneFormer
    10. LLaVA
    11. MiniGPT-4

    AI recommended 11 alternatives but never named JIA-Lab-research/LISA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a multi-modal AI tool for reasoning-based image segmentation tasks.
    you: not recommended
    AI recommended (in order):
    1. Segment Anything Model (SAM)
    2. Grounding DINO
    3. OWL-ViT
    4. CLIPSeg
    5. SEEM
    6. YOLO-World

    AI recommended 6 alternatives but never named JIA-Lab-research/LISA. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of JIA-Lab-research/LISA?
    pass
    AI named JIA-Lab-research/LISA explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts JIA-Lab-research/LISA in production, what risks or prerequisites should they evaluate first?
    pass
    AI named JIA-Lab-research/LISA explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo JIA-Lab-research/LISA solve, and who is the primary audience?
    pass
    AI named JIA-Lab-research/LISA explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of JIA-Lab-research/LISA. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
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JIA-Lab-research/LISA — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite