RRepoGEO

REPOGEO REPORT · LITE

Ruixxxx/Awesome-Vision-Mamba-Models

Default branch main · commit 9286a568 · scanned 6/12/2026, 7:47:17 PM

GitHub: 738 stars · 42 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 Ruixxxx/Awesome-Vision-Mamba-Models, 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
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    vision-mamba, mamba-models, state-space-models, computer-vision, awesome-list, survey, deep-learning
  • highreadme#2
    Reposition README opening to clarify repo's nature

    Why:

    COPY-PASTE FIX
    This repository serves as the official curated collection and survey of literature associated with Mamba models in computer vision, providing new outlooks and tracking the latest advancements. It accompanies our paper, 'Visual Mamba: A Survey and New Outlooks'.
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a new file named `LICENSE` in the repository root with the content of the MIT License.

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 Ruixxxx/Awesome-Vision-Mamba-Models
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Mamba
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Mamba · recommended 2×
  2. VMamba · recommended 2×
  3. Vision Mamba (Vim) · recommended 1×
  4. U-Mamba · recommended 1×
  5. S4 (Structured State Space Sequence Models) · recommended 1×
  • CATEGORY QUERY
    What are the latest advancements in state space models for computer vision tasks?
    you: not recommended
    AI recommended (in order):
    1. Mamba
    2. Vision Mamba (Vim)
    3. VMamba
    4. U-Mamba
    5. S4 (Structured State Space Sequence Models)
    6. S4D (Diagonal S4)
    7. S5 (Simplified State Space Layers)
    8. Hungry Hippo (H3)
    9. Mega (Multiscale Gated Attention)
    10. Bi-Mamba

    AI recommended 10 alternatives but never named Ruixxxx/Awesome-Vision-Mamba-Models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking efficient visual processing models that overcome transformer architecture limitations.
    you: not recommended
    AI recommended (in order):
    1. Mamba
    2. Vision Mamba
    3. VMamba
    4. ConvNeXt
    5. EfficientNetV2
    6. ResNet-RS
    7. MLP-Mixer
    8. ResMLP
    9. gMLP
    10. PoolFormer
    11. ConvFormer
    12. Perceiver IO
    13. Perceiver AR
    14. Swin Transformer V2

    AI recommended 14 alternatives but never named Ruixxxx/Awesome-Vision-Mamba-Models. 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 Ruixxxx/Awesome-Vision-Mamba-Models?
    pass
    AI named Ruixxxx/Awesome-Vision-Mamba-Models explicitly

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

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