RRepoGEO

REPOGEO REPORT · LITE

ttengwang/Awesome_Prompting_Papers_in_Computer_Vision

Default branch main · commit 2a90d303 · scanned 5/29/2026, 7:23:03 PM

GitHub: 926 stars · 68 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 ttengwang/Awesome_Prompting_Papers_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
    Clarify the README's opening sentence to emphasize curation and benefit

    Why:

    CURRENT
    # Awesome Prompting Papers in Computer Vision
A curated list of prompt-based papers in computer vision and vision-language learning.
    COPY-PASTE FIX
    # Awesome Prompting Papers in Computer Vision
A comprehensive, human-curated list of prompt-based research papers in computer vision and vision-language learning, organized to help researchers quickly find and navigate key advancements.
  • highlicense#2
    Add a LICENSE file to the repository root

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository's root directory, choosing a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that aligns with the project's intended usage.
  • mediumabout#3
    Enhance the repository's 'About' description

    Why:

    CURRENT
    A curated list of prompt-based paper in computer vision and vision-language learning.
    COPY-PASTE FIX
    A comprehensive, human-curated list of prompt-based research papers in computer vision and vision-language learning, organized for easy navigation by researchers and practitioners.

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 ttengwang/Awesome_Prompting_Papers_in_Computer_Vision
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LoRA
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LoRA · recommended 1×
  2. peft · recommended 1×
  3. Houlsby Adapters · recommended 1×
  4. Compacter · recommended 1×
  5. VPT · recommended 1×
  • CATEGORY QUERY
    How to adapt large vision models efficiently for new tasks with limited data?
    you: not recommended
    AI recommended (in order):
    1. LoRA
    2. peft
    3. Houlsby Adapters
    4. Compacter
    5. VPT
    6. CoOp/CoCoOp
    7. CLIP
    8. ResNet
    9. Vision Transformer (ViT)
    10. DistilBERT
    11. MAML
    12. ProtoNets
    13. learn2learn
    14. torchmeta
    15. DINOv2
    16. MAE
    17. SimCLR
    18. BYOL

    AI recommended 18 alternatives but never named ttengwang/Awesome_Prompting_Papers_in_Computer_Vision. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find research on visual prompt tuning and vision-language learning methods?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Google Scholar
    3. Semantic Scholar
    4. Papers With Code
    5. OpenReview.net
    6. CVPR
    7. ICCV
    8. ECCV
    9. NeurIPS
    10. ICLR
    11. ICML
    12. EMNLP
    13. ACL
    14. IEEE Xplore
    15. ACM Digital Library
    16. Hugging Face Blog/Papers

    AI recommended 16 alternatives but never named ttengwang/Awesome_Prompting_Papers_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 ttengwang/Awesome_Prompting_Papers_in_Computer_Vision?
    pass
    AI did not name ttengwang/Awesome_Prompting_Papers_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 ttengwang/Awesome_Prompting_Papers_in_Computer_Vision in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ttengwang/Awesome_Prompting_Papers_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 ttengwang/Awesome_Prompting_Papers_in_Computer_Vision solve, and who is the primary audience?
    pass
    AI did not name ttengwang/Awesome_Prompting_Papers_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?

Embed your GEO score

Drop this badge into the README of ttengwang/Awesome_Prompting_Papers_in_Computer_Vision. 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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ttengwang/Awesome_Prompting_Papers_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