RRepoGEO

REPOGEO REPORT · LITE

PKU-YuanGroup/LanguageBind

Default branch main · commit 7070c533 · scanned 6/8/2026, 2:08:08 PM

GitHub: 881 stars · 60 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/LanguageBind, 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
    Add a clear project type statement to the README's opening

    Why:

    CURRENT
    The README currently starts with a centered paper title.
    COPY-PASTE FIX
    Add this sentence as the very first line of text in the README: "LanguageBind is an N-modality pretraining framework that extends video-language models through language-based semantic alignment."
  • hightopics#2
    Expand topics to include N-modality and specific alignment keywords

    Why:

    CURRENT
    language-central, multi-modal, pretraining, zero-shot
    COPY-PASTE FIX
    language-central, multi-modal, pretraining, zero-shot, n-modality, semantic-alignment, video-language, audio-language
  • mediumabout#3
    Rephrase the 'About' description for directness and clarity on project type

    Why:

    CURRENT
    【ICLR 2024🔥】 Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment
    COPY-PASTE FIX
    LanguageBind: An ICLR 2024 N-modality pretraining framework, extending video-language models via language-based semantic alignment.

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/LanguageBind
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI CLIP
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI CLIP · recommended 1×
  2. Google ALIGN · recommended 1×
  3. Meta FLAVA · recommended 1×
  4. Google CoCa · recommended 1×
  5. Microsoft BEiT-3 · recommended 1×
  • CATEGORY QUERY
    How to pretrain models for zero-shot tasks across multiple modalities using language alignment?
    you: not recommended
    AI recommended (in order):
    1. OpenAI CLIP
    2. Google ALIGN
    3. Meta FLAVA
    4. Google CoCa
    5. Microsoft BEiT-3
    6. LAION-5B

    AI recommended 6 alternatives but never named PKU-YuanGroup/LanguageBind. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework to extend video-language models for N-modality semantic alignment.
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    2. Hugging Face Transformers (huggingface/transformers)
    3. MMAction2 (open-mmlab/mmaction2)
    4. Detectron2 (facebookresearch/detectron2)
    5. TensorFlow (tensorflow/tensorflow)
    6. JAX (google/jax)

    AI recommended 6 alternatives but never named PKU-YuanGroup/LanguageBind. 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/LanguageBind?
    pass
    AI named PKU-YuanGroup/LanguageBind 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/LanguageBind in production, what risks or prerequisites should they evaluate first?
    pass
    AI named PKU-YuanGroup/LanguageBind 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/LanguageBind solve, and who is the primary audience?
    pass
    AI named PKU-YuanGroup/LanguageBind 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/LanguageBind. 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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MARKDOWN (README)
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HTML
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PKU-YuanGroup/LanguageBind — 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