RRepoGEO

REPOGEO REPORT · LITE

Computer-Vision-in-the-Wild/CVinW_Readings

Default branch main · commit 0f6536c3 · scanned 5/24/2026, 12:03:56 PM

GitHub: 1,369 stars · 57 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 Computer-Vision-in-the-Wild/CVinW_Readings, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Clarify the repository's nature in the README's opening paragraph

    Why:

    CURRENT
    ``Computer Vision in the Wild (CVinW)'' is an emerging research field. This writeup provides a quick introduction of CVinW and maintains a collection of papers on the topic.
    COPY-PASTE FIX
    This repository is a curated and maintained collection of academic papers and resources focused on ``Computer Vision in the Wild (CVinW)'', an emerging research field. It provides a quick introduction to CVinW and organizes key literature for researchers and practitioners.
  • mediumlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Add a LICENSE file to the repository root, choosing an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that reflects how you want others to use this collection.

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 Computer-Vision-in-the-Wild/CVinW_Readings
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Scholar
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Scholar · recommended 1×
  2. arXiv · recommended 1×
  3. CVF Open Access · recommended 1×
  4. CVPR · recommended 1×
  5. ICCV · recommended 1×
  • CATEGORY QUERY
    Where can I find academic papers on computer vision models for unconstrained, real-world scenarios?
    you: not recommended
    AI recommended (in order):
    1. Google Scholar
    2. arXiv
    3. CVF Open Access
    4. CVPR
    5. ICCV
    6. ECCV
    7. Semantic Scholar
    8. Microsoft Academic
    9. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
    10. International Journal of Computer Vision (IJCV)
    11. Computer Vision and Image Understanding (CVIU)
    12. ResearchGate
    13. Academia.edu

    AI recommended 13 alternatives but never named Computer-Vision-in-the-Wild/CVinW_Readings. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the challenges and solutions for deploying computer vision models in diverse, uncontrolled environments?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning
    2. TensorFlow Extended (TFX)
    3. Weights & Biases (W&B)
    4. NVIDIA TensorRT
    5. OpenVINO Toolkit (Intel)
    6. ONNX Runtime
    7. Edge TPU (Google Coral)
    8. Adversarial Robustness Toolbox (ART) (IBM)
    9. CleverHans
    10. Kubeflow
    11. MLflow
    12. AWS SageMaker
    13. SHAP (SHapley Additive exPlanations)
    14. LIME (Local Interpretable Model-agnostic Explanations)

    AI recommended 14 alternatives but never named Computer-Vision-in-the-Wild/CVinW_Readings. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 Computer-Vision-in-the-Wild/CVinW_Readings?
    pass
    AI named Computer-Vision-in-the-Wild/CVinW_Readings explicitly

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

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