RRepoGEO

REPOGEO REPORT · LITE

krantiparida/awesome-audio-visual

Default branch master · commit 257f1986 · scanned 6/15/2026, 1:18:54 AM

GitHub: 772 stars · 66 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 krantiparida/awesome-audio-visual, 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 README's opening sentence to emphasize AI research

    Why:

    CURRENT
    A curated list of papers and datsets for various audio-visual tasks, inspired by awesome-computer-vision.
    COPY-PASTE FIX
    A curated list of research papers and datasets for various audio-visual AI tasks, inspired by awesome-computer-vision.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0). If unsure, consult legal advice or choose a permissive license like MIT.
  • mediumtopics#3
    Correct typo in 'mutli-modal' topic

    Why:

    CURRENT
    audio-visual, awesome, awesome-list, cross-modal, localization, mutli-modal, source-separation
    COPY-PASTE FIX
    audio-visual, awesome, awesome-list, cross-modal, localization, multi-modal, source-separation

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 krantiparida/awesome-audio-visual
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Papers With Code
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Papers With Code · recommended 1×
  2. Awesome Audio-Visual Learning · recommended 1×
  3. arXiv · recommended 1×
  4. Google Scholar · recommended 1×
  5. CVPR · recommended 1×
  • CATEGORY QUERY
    Where can I find a curated list of research papers for audio-visual AI tasks?
    you: not recommended
    AI recommended (in order):
    1. Papers With Code
    2. Awesome Audio-Visual Learning
    3. arXiv
    4. Google Scholar
    5. CVPR
    6. ICCV
    7. ECCV
    8. NeurIPS
    9. ICML
    10. Interspeech
    11. ICASSP
    12. Google AI
    13. Meta AI
    14. Microsoft Research
    15. Stanford
    16. CMU
    17. MIT
    18. Oxford

    AI recommended 18 alternatives but never named krantiparida/awesome-audio-visual. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the essential datasets and papers for cross-modal audio-visual processing?
    you: not recommended
    AI recommended (in order):
    1. AudioSet
    2. VGGSound
    3. LRS3-TED / LRS2-TED
    4. AVE Dataset
    5. CREMA-D
    6. Look, Listen and Learn
    7. Objects that Sound
    8. Audio-Visual Speech Separation Using an Attention-Based U-Net
    9. Learning to Localize Sound Sources in Visual Scenes
    10. Sound-Guided Semantic Video Generation
    11. AV-HuBERT
    12. Audio-Visual Event Localization in the Wild

    AI recommended 12 alternatives but never named krantiparida/awesome-audio-visual. 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 krantiparida/awesome-audio-visual?
    pass
    AI named krantiparida/awesome-audio-visual explicitly

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

  • If a team adopts krantiparida/awesome-audio-visual in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name krantiparida/awesome-audio-visual — 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?

  • In one sentence, what problem does the repo krantiparida/awesome-audio-visual solve, and who is the primary audience?
    pass
    AI did not name krantiparida/awesome-audio-visual — 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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MARKDOWN (README)
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krantiparida/awesome-audio-visual — 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