RRepoGEO

REPOGEO REPORT · LITE

vulab-AI/Awesome-Spatial-VLMs

Default branch main · commit bd978804 · scanned 6/6/2026, 6:47:25 PM

GitHub: 551 stars · 2 forks

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 vulab-AI/Awesome-Spatial-VLMs, 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 the README opening to emphasize its role as a definitive resource

    Why:

    CURRENT
    >A curated hub for Spatial Intelligence in Vision-Language Models. Actively maintained—watch for updates, benchmark your VLM with our evaluation code, and consider starring 🌟 and sharing if helpful. This repository is the official, community-maintained resource for our survey paper: **Spatial Intelligence in Vision-Language Models: A Comprehensive Survey**.
    COPY-PASTE FIX
    This is the **definitive, community-maintained hub** for Spatial Intelligence in Vision-Language Models, serving as the official resource for our comprehensive survey paper. It centralizes papers, benchmarks, and evaluation code, making it the go-to collection for researchers and developers.
  • hightopics#2
    Add more specific topics related to benchmarks, evaluation, and paper collection

    Why:

    CURRENT
    awesome-list, awesome-spatial-vlms, mllm, spatial-intelligence, spatial-reasoning, survey, vision-language-model, vlm
    COPY-PASTE FIX
    awesome-list, awesome-spatial-vlms, mllm, spatial-intelligence, spatial-reasoning, survey, vision-language-model, vlm, vlm-benchmarks, vlm-evaluation, paper-collection, research-resources
  • mediumreadme#3
    Add a dedicated section in the README for Benchmarks & Evaluation

    Why:

    COPY-PASTE FIX
    ## Benchmarks & Evaluation

This repository provides comprehensive resources for benchmarking and evaluating Spatial Vision-Language Models.
*   **Evaluation Code:** Explore our official evaluation framework [here](https://github.com/vulab-AI/Awesome-Spatial-VLMs/blob/main/evaluation/README.md).
*   **Spatial VQA Datasets:** Access curated datasets for Spatial VQA [here](https://github.com/vulab-AI/Awesome-Spatial-VLMs/blob/main/data_benchmark/Dataset_SVQA.md).
*   **Spatial VQA Benchmarks:** Review detailed benchmarks for Spatial VQA [here](https://github.com/vulab-AI/Awesome-Spatial-VLMs/blob/main/data_benchmark/Benchmark_SVQA.md).

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 vulab-AI/Awesome-Spatial-VLMs
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.org · recommended 1×
  3. Semantic Scholar · recommended 1×
  4. Papers With Code · recommended 1×
  5. CVPR · recommended 1×
  • CATEGORY QUERY
    How can I find resources and papers on spatial reasoning in vision-language models?
    you: not recommended
    AI recommended (in order):
    1. Google Scholar
    2. arXiv.org
    3. Semantic Scholar
    4. Papers With Code
    5. CVPR
    6. ICCV
    7. ECCV
    8. NeurIPS
    9. ICLR
    10. ACL
    11. GitHub
    12. Stanford Vision and Learning Lab
    13. UC Berkeley AI Research (BAIR)
    14. Allen Institute for AI (AI2)
    15. Meta AI Research (FAIR)
    16. Google DeepMind
    17. Google AI

    AI recommended 17 alternatives but never named vulab-AI/Awesome-Spatial-VLMs. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find benchmarks and evaluation methods for spatial intelligence in VLMs?
    you: not recommended
    AI recommended (in order):
    1. GQA
    2. CLEVR
    3. NLVR2
    4. OK-VQA
    5. Touchdown
    6. ALFRED
    7. VISPROG

    AI recommended 7 alternatives but never named vulab-AI/Awesome-Spatial-VLMs. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 vulab-AI/Awesome-Spatial-VLMs?
    pass
    AI did not name vulab-AI/Awesome-Spatial-VLMs — 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 vulab-AI/Awesome-Spatial-VLMs in production, what risks or prerequisites should they evaluate first?
    pass
    AI named vulab-AI/Awesome-Spatial-VLMs 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 vulab-AI/Awesome-Spatial-VLMs solve, and who is the primary audience?
    pass
    AI did not name vulab-AI/Awesome-Spatial-VLMs — 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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  • Brand-free category queries5 vs 2 in Lite
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