RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/LaViLa

Default branch main · commit 8002b5ab · scanned 6/6/2026, 3:12:51 PM

GitHub: 533 stars · 47 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 facebookresearch/LaViLa, 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

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

OVERALL DIRECTION
  • highreadme#1
    Clarify the README's opening to emphasize its research framework nature

    Why:

    CURRENT
    LaViLa (Language augmented Video Language Pretraining) is a new approach to learning video representations from Large Language Models (LLMs). We repurpose LLMs to be visually conditioned "Narrators", and use them to automatically generate video-language paired data. We use this data to then learn a video-langauge representation, outperforming prior work by large margins.
    COPY-PASTE FIX
    LaViLa (Language augmented Video Language Pretraining) is a new approach to learning video representations from Large Language Models (LLMs). This **research framework** repurposes LLMs to be visually conditioned "Narrators", using them to automatically generate video-language paired data. We then leverage this data to learn a video-language representation, outperforming prior work by large margins.
  • mediumhomepage#2
    Add the project homepage URL

    Why:

    COPY-PASTE FIX
    https://lavila.github.io/

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 facebookresearch/LaViLa
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
YouTube's Automatic Captions & Description Generation
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. YouTube's Automatic Captions & Description Generation · recommended 1×
  2. Google Cloud Video AI · recommended 1×
  3. AWS Rekognition Video · recommended 1×
  4. Azure Video Indexer · recommended 1×
  5. OpenAI's GPT-4o · recommended 1×
  • CATEGORY QUERY
    What's the best way to automatically generate detailed text descriptions for video content?
    you: not recommended
    AI recommended (in order):
    1. YouTube's Automatic Captions & Description Generation
    2. Google Cloud Video AI
    3. AWS Rekognition Video
    4. Azure Video Indexer
    5. OpenAI's GPT-4o
    6. Claude 3 Opus
    7. AssemblyAI
    8. Hugging Face Transformers

    AI recommended 8 alternatives but never named facebookresearch/LaViLa. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I leverage large language models to improve video understanding and representation learning?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4V (Vision)
    2. Google Gemini (Pro Vision / Ultra)
    3. Meta LLaVA (Large Language and Vision Assistant)
    4. CLIP (Contrastive Language-Image Pre-training)
    5. Llama 2
    6. Mistral
    7. BLIP-2 (Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models)
    8. Video-LLaMA
    9. InternVideo (e.g., InternVideo2)

    AI recommended 9 alternatives but never named facebookresearch/LaViLa. 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 facebookresearch/LaViLa?
    pass
    AI named facebookresearch/LaViLa explicitly

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

  • If a team adopts facebookresearch/LaViLa in production, what risks or prerequisites should they evaluate first?
    pass
    AI named facebookresearch/LaViLa 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 facebookresearch/LaViLa solve, and who is the primary audience?
    pass
    AI named facebookresearch/LaViLa explicitly

    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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MARKDOWN (README)
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facebookresearch/LaViLa — 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