RRepoGEO

REPOGEO REPORT · LITE

jingyi0000/VLM_survey

Default branch main · commit e7f12322 · scanned 5/15/2026, 6:53:09 PM

GitHub: 3,117 stars · 233 forks

AI VISIBILITY SCORE
28 /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
2 / 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 jingyi0000/VLM_survey, 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
  • highabout#1
    Update the 'About' description to clarify repo's identity as a survey paper's official repository

    Why:

    CURRENT
    Collection of AWESOME vision-language models for vision tasks
    COPY-PASTE FIX
    Official repository for 'Vision-Language Models for Vision Tasks: A Survey' (TPAMI 2024), offering a systematic collection of VLM studies for visual recognition tasks.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT License) in the repository root to clarify usage rights.
  • mediumhomepage#3
    Add the survey paper's link as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2304.00685

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 jingyi0000/VLM_survey
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Papers with Code
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Papers with Code · recommended 1×
  2. Hugging Face Transformers Library · recommended 1×
  3. CLIP · recommended 1×
  4. ViLT · recommended 1×
  5. BLIP · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive overview of vision-language models for computer vision tasks?
    you: not recommended
    AI recommended (in order):
    1. Papers with Code
    2. Hugging Face Transformers Library

    AI recommended 2 alternatives but never named jingyi0000/VLM_survey. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the leading multi-modal deep learning models for various visual recognition problems?
    you: not recommended
    AI recommended (in order):
    1. CLIP
    2. ViLT
    3. BLIP
    4. Flamingo
    5. CoCa
    6. ALBEF
    7. OpenCLIP

    AI recommended 7 alternatives but never named jingyi0000/VLM_survey. 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 jingyi0000/VLM_survey?
    pass
    AI named jingyi0000/VLM_survey explicitly

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

  • If a team adopts jingyi0000/VLM_survey in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jingyi0000/VLM_survey 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 jingyi0000/VLM_survey solve, and who is the primary audience?
    pass
    AI did not name jingyi0000/VLM_survey — 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

Drop this badge into the README of jingyi0000/VLM_survey. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/jingyi0000/VLM_survey.svg)](https://repogeo.com/en/r/jingyi0000/VLM_survey)
HTML
<a href="https://repogeo.com/en/r/jingyi0000/VLM_survey"><img src="https://repogeo.com/badge/jingyi0000/VLM_survey.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

jingyi0000/VLM_survey — 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