RRepoGEO

REPOGEO REPORT · LITE

jingyi0000/VLM_survey

Default branch main · commit e7f12322 · scanned 6/26/2026, 1:22:46 PM

GitHub: 3,127 stars · 236 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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
  • highreadme#1
    Reposition the README's opening to clarify its purpose as a curated list/survey

    Why:

    CURRENT
    This is the repository of **Vision Language Models for Vision Tasks: a Survey**, a systematic survey of VLM studies in various visual recognition tasks including image classification, object detection, semantic segmentation, etc.
    COPY-PASTE FIX
    This repository provides a comprehensive, curated list of **Vision-Language Models (VLMs) for Vision Tasks**, serving as a companion to our systematic survey paper. It covers VLM studies in various visual recognition tasks including image classification, object detection, semantic segmentation, and more.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, choosing an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) to clarify usage rights.
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Set the repository's homepage URL to the associated survey paper on arXiv: `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
Awesome-Vision-Language-Pretraining
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Awesome-Vision-Language-Pretraining · recommended 1×
  2. Papers With Code · recommended 1×
  3. Hugging Face · recommended 1×
  4. CLIP · recommended 1×
  5. BLIP · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive overview of vision-language models for computer vision?
    you: not recommended
    AI recommended (in order):
    1. Awesome-Vision-Language-Pretraining
    2. Papers With Code
    3. Hugging Face
    4. CLIP
    5. BLIP
    6. ViLT
    7. LLaVA
    8. PaLM-E

    AI recommended 8 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 models for image classification and object detection tasks?
    you: not recommended
    AI recommended (in order):
    1. YOLO (You Only Look Once) series
    2. CLIP (Contrastive Language-Image Pre-training)
    3. OWL-ViT (Open-World Localization with Vision Transformers)
    4. DETR (Detection Transformer)
    5. Grounding DINO
    6. Florence-2

    AI recommended 6 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?

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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