RRepoGEO

REPOGEO REPORT · LITE

PKU-YuanGroup/Video-LLaVA

Default branch main · commit 984e65bf · scanned 5/25/2026, 3:22:48 AM

GitHub: 3,488 stars · 250 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 PKU-YuanGroup/Video-LLaVA, 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
  • hightopics#1
    Add specific video-related topics

    Why:

    CURRENT
    instruction-tuning, large-vision-language-model, multi-modal
    COPY-PASTE FIX
    instruction-tuning, large-vision-language-model, multi-modal, video-llm, video-understanding, video-qa
  • highreadme#2
    Reposition the README's core value proposition

    Why:

    CURRENT
    The README starts with a title followed by many badges and links, pushing descriptive text further down.
    COPY-PASTE FIX
    Immediately after the main H1 title, add a concise sentence like: 'Video-LLaVA is an open-source research framework extending large language models to comprehend and reason about dynamic video content, enabling advanced video question-answering and analysis.'
  • mediumabout#3
    Expand the repository description to clarify its role

    Why:

    CURRENT
    【EMNLP 2024🔥】Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
    COPY-PASTE FIX
    Video-LLaVA is an open-source research project and framework presented at EMNLP 2024, enabling large language models to understand and reason about video content through a novel alignment-before-projection approach. It supports video question-answering and multi-modal video analysis.

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 PKU-YuanGroup/Video-LLaVA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Gemini
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Gemini · recommended 2×
  2. OpenAI GPT-4o · recommended 2×
  3. Microsoft Copilot · recommended 2×
  4. Llama 3 · recommended 1×
  5. AWS Rekognition Video · recommended 1×
  • CATEGORY QUERY
    Need an AI model to comprehend video content and generate descriptive text responses.
    you: not recommended
    AI recommended (in order):
    1. Google Gemini
    2. OpenAI GPT-4o
    3. Llama 3
    4. Microsoft Copilot
    5. AWS Rekognition Video
    6. Amazon Titan
    7. Claude
    8. Azure AI Video Indexer
    9. Azure OpenAI Service
    10. Hugging Face Transformers
    11. BLIP-2
    12. Video-LLaMA

    AI recommended 12 alternatives but never named PKU-YuanGroup/Video-LLaVA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a large multi-modal model that can follow instructions for video analysis tasks.
    you: not recommended
    AI recommended (in order):
    1. Google Gemini
    2. OpenAI GPT-4o
    3. Meta Llama 3
    4. Microsoft Copilot
    5. InternVideo2

    AI recommended 5 alternatives but never named PKU-YuanGroup/Video-LLaVA. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 PKU-YuanGroup/Video-LLaVA?
    pass
    AI named PKU-YuanGroup/Video-LLaVA explicitly

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

  • If a team adopts PKU-YuanGroup/Video-LLaVA in production, what risks or prerequisites should they evaluate first?
    pass
    AI named PKU-YuanGroup/Video-LLaVA 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 PKU-YuanGroup/Video-LLaVA solve, and who is the primary audience?
    pass
    AI named PKU-YuanGroup/Video-LLaVA 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 PKU-YuanGroup/Video-LLaVA. 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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PKU-YuanGroup/Video-LLaVA — 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