RRepoGEO

REPOGEO REPORT · LITE

zengyan-97/X-VLM

Default branch master · commit cb4fff15 · scanned 6/4/2026, 12:48:23 PM

GitHub: 506 stars · 52 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 zengyan-97/X-VLM, 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
    Clarify the README's opening statement to position X-VLM as a research implementation

    Why:

    CURRENT
    # X-VLM: learning multi-grained vision language alignments
    COPY-PASTE FIX
    # X-VLM: Official PyTorch Implementation of Multi-Grained Vision Language Pre-Training (ICML 2022)

This repository provides the official PyTorch implementation for X-VLM: Multi-Grained Vision Language Pre-Training (ICML 2022).
  • mediumhomepage#2
    Add the paper's arXiv link to the repository homepage field

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2111.13778
  • mediumtopics#3
    Expand repository topics with more specific keywords for multimodal AI

    Why:

    CURRENT
    multimodality, vision-and-language, x-vlm
    COPY-PASTE FIX
    multimodality, vision-and-language, x-vlm, cross-modal, vision-language-models, image-text-alignment, video-text-alignment, deep-learning-research

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 zengyan-97/X-VLM
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. Facebook DALL-E 2 · recommended 1×
  4. Stable Diffusion · recommended 1×
  5. Hugging Face Transformers Library · recommended 1×
  • CATEGORY QUERY
    How can I effectively align text descriptions with visual content for multimodal understanding?
    you: not recommended
    AI recommended (in order):
    1. OpenAI CLIP
    2. Google ALIGN
    3. Facebook DALL-E 2
    4. Stable Diffusion
    5. Hugging Face Transformers Library
    6. PyTorch-Image-Models (timm)
    7. TensorFlow Hub
    8. Keras Applications
    9. MMF

    AI recommended 9 alternatives but never named zengyan-97/X-VLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best models for cross-modal understanding across images, video, and text data?
    you: not recommended
    AI recommended (in order):
    1. OpenAI's CLIP
    2. Google's Flamingo
    3. Meta's Data2vec
    4. Microsoft's Florence
    5. Google's PaLM-E
    6. DeepMind's Perceiver IO
    7. OpenAI's DALL-E 3

    AI recommended 7 alternatives but never named zengyan-97/X-VLM. 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 zengyan-97/X-VLM?
    pass
    AI named zengyan-97/X-VLM explicitly

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

  • If a team adopts zengyan-97/X-VLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named zengyan-97/X-VLM 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 zengyan-97/X-VLM solve, and who is the primary audience?
    pass
    AI named zengyan-97/X-VLM 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 zengyan-97/X-VLM. 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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zengyan-97/X-VLM — 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
zengyan-97/X-VLM — RepoGEO report