RRepoGEO

REPOGEO REPORT · LITE

muzairkhattak/multimodal-prompt-learning

Default branch main · commit 69bce21a · scanned 6/16/2026, 7:17:24 AM

GitHub: 821 stars · 65 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 muzairkhattak/multimodal-prompt-learning, 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 the repository

    Why:

    COPY-PASTE FIX
    multimodal, prompt-learning, vision-language-models, cvpr2023, deep-learning, machine-learning, computer-vision, nlp, research-paper, pytorch
  • highreadme#2
    Strengthen the README's opening to clarify the repo's nature as a research implementation

    Why:

    CURRENT
    Official implementation of the paper "MaPLe: Multi-modal Prompt Learning".
    COPY-PASTE FIX
    This repository provides the official PyTorch implementation for "MaPLe: Multi-modal Prompt Learning," a CVPR 2023 paper introducing a novel method for learning prompts simultaneously across vision and language modalities to enhance cross-modal understanding and generalization in Vision-Language Models (VLMs).
  • mediumabout#3
    Expand the repository description with key terms

    Why:

    CURRENT
    [CVPR 2023] Official repository of paper titled "MaPLe: Multi-modal Prompt Learning".
    COPY-PASTE FIX
    Official PyTorch implementation of "MaPLe: Multi-modal Prompt Learning" (CVPR 2023), a novel method for learning prompts in vision and language modalities to improve VLM generalization and cross-modal understanding.

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 muzairkhattak/multimodal-prompt-learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. OpenAI's CLIP · recommended 1×
  3. DALL-E 2/3 · recommended 1×
  4. CLIP · recommended 1×
  5. DALL-E 2 · recommended 1×
  • CATEGORY QUERY
    How can I effectively learn prompts for deep learning models processing multiple data modalities?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library (huggingface/transformers)
    2. OpenAI's CLIP
    3. DALL-E 2/3

    AI recommended 3 alternatives but never named muzairkhattak/multimodal-prompt-learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking methods to improve cross-modal understanding and generalization using prompt-based learning.
    you: not recommended
    AI recommended (in order):
    1. CLIP
    2. DALL-E 2
    3. Stable Diffusion
    4. Flamingo
    5. CoCa
    6. VL-T5
    7. BLIP
    8. LAVIS

    AI recommended 8 alternatives but never named muzairkhattak/multimodal-prompt-learning. 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 muzairkhattak/multimodal-prompt-learning?
    pass
    AI did not name muzairkhattak/multimodal-prompt-learning — 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 muzairkhattak/multimodal-prompt-learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named muzairkhattak/multimodal-prompt-learning 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 muzairkhattak/multimodal-prompt-learning solve, and who is the primary audience?
    pass
    AI did not name muzairkhattak/multimodal-prompt-learning — 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 muzairkhattak/multimodal-prompt-learning. 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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MARKDOWN (README)
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muzairkhattak/multimodal-prompt-learning — 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