RRepoGEO

REPOGEO REPORT · LITE

zli12321/Vision-Language-Models-Overview

Default branch main · commit cf18731a · scanned 6/8/2026, 6:13:11 AM

GitHub: 616 stars · 37 forks

AI VISIBILITY SCORE
22 /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
1 / 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 zli12321/Vision-Language-Models-Overview, 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
    Reposition README H1 to clearly state 'Survey' or 'Overview'

    Why:

    CURRENT
    # Benchmark and Evaluations, RL Alignment, Applications, and Challenges of Large Vision Language Models
    COPY-PASTE FIX
    # Vision-Language Models Overview: A Comprehensive Survey of Architectures, Benchmarks, and Applications
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • highhomepage#3
    Add the project homepage to the repository metadata

    Why:

    COPY-PASTE FIX
    https://zli12321.github.io/VLM_Survey/

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 zli12321/Vision-Language-Models-Overview
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
CLIP
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. CLIP · recommended 2×
  2. Flamingo · recommended 2×
  3. LLaVA · recommended 2×
  4. Papers With Code · recommended 1×
  5. Hugging Face Transformers Library · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive overview of current vision-language model architectures and applications?
    you: not recommended
    AI recommended (in order):
    1. Papers With Code
    2. Hugging Face Transformers Library
    3. CLIP
    4. BLIP
    5. ViLT
    6. Flamingo
    7. LLaVA
    8. arXiv
    9. OpenAI Blog
    10. CLIP
    11. DALL-E 2
    12. GPT-4V (ision)
    13. Google AI Blog
    14. PaLM-E
    15. Gemini
    16. Vision-Language Pre-training: A Survey
    17. Towards Data Science (Medium)
    18. Analytics Vidhya

    AI recommended 18 alternatives but never named zli12321/Vision-Language-Models-Overview. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest advancements and benchmarks for multimodal AI models generating text from images?
    you: not recommended
    AI recommended (in order):
    1. GPT-4V
    2. LLaVA
    3. BLIP-2
    4. InstructBLIP
    5. CoCa
    6. Flamingo

    AI recommended 6 alternatives but never named zli12321/Vision-Language-Models-Overview. 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 zli12321/Vision-Language-Models-Overview?
    pass
    AI did not name zli12321/Vision-Language-Models-Overview — likely talking about a different project

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

  • If a team adopts zli12321/Vision-Language-Models-Overview in production, what risks or prerequisites should they evaluate first?
    pass
    AI named zli12321/Vision-Language-Models-Overview 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 zli12321/Vision-Language-Models-Overview solve, and who is the primary audience?
    pass
    AI did not name zli12321/Vision-Language-Models-Overview — likely talking about a different project

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

Embed your GEO score

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  • Brand-free category queries5 vs 2 in Lite
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