RRepoGEO

REPOGEO REPORT · LITE

dk-liang/Awesome-Visual-Transformer

Default branch main · commit d7617b3e · scanned 5/16/2026, 4:52:57 PM

GitHub: 3,583 stars · 406 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /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
2 / 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 dk-liang/Awesome-Visual-Transformer, 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 repo's nature as a curated list in README opening

    Why:

    CURRENT
    Collect some Transformer with Computer-Vision (CV) papers.
    COPY-PASTE FIX
    This is a curated "awesome list" collecting key research papers, code, and resources on Transformers applied to Computer Vision (CV).
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Choose and add a standard open-source license file (e.g., MIT, Apache-2.0, GPL-3.0) to the repository root.
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Set the repository homepage URL to a relevant link, such as the GitHub repository URL itself (https://github.com/dk-liang/Awesome-Visual-Transformer) or a dedicated project page if one exists.

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 dk-liang/Awesome-Visual-Transformer
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
arXiv.org
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. arXiv.org · recommended 1×
  2. Google Scholar · recommended 1×
  3. Papers With Code · recommended 1×
  4. Semantic Scholar · recommended 1×
  5. IEEE Xplore Digital Library · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive collection of research papers on visual transformers?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Google Scholar
    3. Papers With Code
    4. Semantic Scholar
    5. IEEE Xplore Digital Library
    6. ACM Digital Library
    7. OpenReview.net

    AI recommended 7 alternatives but never named dk-liang/Awesome-Visual-Transformer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest advancements and surveys in applying transformers to computer vision tasks?
    you: not recommended
    AI recommended (in order):
    1. Vision Transformers (ViT)
    2. DeiT (Data-efficient Image Transformers)
    3. Swin Transformer
    4. PVT (Pyramid Vision Transformer)
    5. DETR (DEtection TRansformer)
    6. Deformable DETR
    7. DINO (DETR with Improved deNoising anchOr boxes)
    8. Mask2Former
    9. SegFormer
    10. ViViT (Video Vision Transformer)
    11. MViT (Multiscale Vision Transformers)
    12. MAE (Masked Autoencoders Are Scalable Vision Learners)
    13. DINO (Emerging Properties in Self-Supervised Vision Transformers)

    AI recommended 13 alternatives but never named dk-liang/Awesome-Visual-Transformer. 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 dk-liang/Awesome-Visual-Transformer?
    pass
    AI named dk-liang/Awesome-Visual-Transformer explicitly

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

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

Embed your GEO score

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dk-liang/Awesome-Visual-Transformer — 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