RRepoGEO

REPOGEO REPORT · LITE

Yangzhangcst/Transformer-in-Computer-Vision

Default branch main · commit 12aae994 · scanned 5/16/2026, 4:08:04 PM

GitHub: 1,452 stars · 152 forks

AI VISIBILITY SCORE
22 /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
1 / 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 Yangzhangcst/Transformer-in-Computer-Vision, 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 README's opening to clearly state it's an 'awesome list'

    Why:

    CURRENT
    Transformer-in-Vision
A paper list of some recent Transformer-based CV works.
    COPY-PASTE FIX
    # Awesome Transformer-in-Vision: A Curated List of Recent Transformer-based CV Works
This repository is an actively maintained awesome list, providing a comprehensive collection of recent research papers and their associated code (where available) on Transformer models applied to Computer Vision tasks.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the text of a common open-source license like MIT or Apache-2.0, or clearly state the intended license(s) directly in the README.
  • mediumhomepage#3
    Add the repository URL to the 'Homepage' field in the About section

    Why:

    COPY-PASTE FIX
    https://github.com/Yangzhangcst/Transformer-in-Computer-Vision

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 Yangzhangcst/Transformer-in-Computer-Vision
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Papers With Code
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Papers With Code · recommended 1×
  2. arXiv · recommended 1×
  3. Awesome-Vision-Transformer · recommended 1×
  4. Google Scholar · recommended 1×
  5. Distill.pub · recommended 1×
  • CATEGORY QUERY
    Where can I find a curated list of recent research papers on Transformers for computer vision?
    you: not recommended
    AI recommended (in order):
    1. Papers With Code
    2. arXiv
    3. Awesome-Vision-Transformer
    4. Google Scholar
    5. Distill.pub
    6. The Batch by DeepLearning.AI
    7. Import AI by Jack Clark
    8. CVPR
    9. ICCV
    10. ECCV
    11. NeurIPS
    12. ICML

    AI recommended 12 alternatives but never named Yangzhangcst/Transformer-in-Computer-Vision. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    I need an awesome list of deep learning papers applying transformer models to image tasks.
    you: not recommended
    AI recommended (in order):
    1. Vision Transformer (ViT)
    2. Swin Transformer
    3. DETR
    4. Masked Autoencoders (MAE)
    5. Perceiver IO
    6. U-Net Transformer (UNETR)
    7. Generative Pretraining from Pixels (DALLE)

    AI recommended 7 alternatives but never named Yangzhangcst/Transformer-in-Computer-Vision. 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 Yangzhangcst/Transformer-in-Computer-Vision?
    pass
    AI did not name Yangzhangcst/Transformer-in-Computer-Vision — 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?

  • If a team adopts Yangzhangcst/Transformer-in-Computer-Vision in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Yangzhangcst/Transformer-in-Computer-Vision 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 Yangzhangcst/Transformer-in-Computer-Vision solve, and who is the primary audience?
    pass
    AI did not name Yangzhangcst/Transformer-in-Computer-Vision — 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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Yangzhangcst/Transformer-in-Computer-Vision — 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