REPOGEO REPORT · LITE

pliang279/awesome-multimodal-ml

Default branch master · commit f1d3bae4 · scanned 5/25/2026, 4:42:32 AM

GitHub: 6,876 stars · 902 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 pliang279/awesome-multimodal-ml, 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's opening to clearly state its 'awesome list' nature

Why:

CURRENT

# Awesome Multimodal Machine Learning

By Paul Liang (pliang@cs.cmu.edu), Machine Learning Department and Language Technologies Institute, CMU, with help from members of the MultiComp Lab at LTI, CMU. If there are any areas, papers, and datasets I missed, please let me know!

## Course content + workshops

COPY-PASTE FIX

# Awesome Multimodal Machine Learning

A curated and comprehensive reading list for research topics in multimodal machine learning, maintained by Paul Liang (pliang@cs.cmu.edu) and members of the MultiComp Lab at LTI, CMU. This repository aims to be the definitive resource for papers, datasets, and projects in the field. If there are any areas, papers, and datasets I missed, please let me know!

## Course content + workshops

mediumhomepage#2
Add the repository URL as the homepage in the About section
Why:
COPY-PASTE FIX
```
https://github.com/pliang279/awesome-multimodal-ml
```

lowtopics#3

Add 'awesome-list' and 'awesome' to the repository topics

Why:

CURRENT

computer-vision, deep-learning, healthcare, machine-learning, multimodal-learning, natural-language-processing, reading-list, reinforcement-learning, representation-learning, robotics, speech-processing

COPY-PASTE FIX

computer-vision, deep-learning, healthcare, machine-learning, multimodal-learning, natural-language-processing, reading-list, reinforcement-learning, representation-learning, robotics, speech-processing, awesome-list, awesome

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 pliang279/awesome-multimodal-ml

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Awesome Multimodal Learning Repository

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Awesome Multimodal Learning Repository · recommended 1×
Papers With Code · recommended 1×
CVPR · recommended 1×
ICCV · recommended 1×
NeurIPS · recommended 1×

CATEGORY QUERY
Where can I find a comprehensive reading list for multimodal AI research topics?
you: not recommended
AI recommended (in order):
1. Awesome Multimodal Learning Repository
2. Papers With Code
3. CVPR
4. ICCV
5. NeurIPS
6. ICML
7. EMNLP
8. ACL
9. Google Scholar
10. ArXiv
AI recommended 10 alternatives but never named pliang279/awesome-multimodal-ml. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What resources explain the core concepts and challenges in multimodal machine learning?
you: not recommended
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 pliang279/awesome-multimodal-ml?
pass
AI did not name pliang279/awesome-multimodal-ml — 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 pliang279/awesome-multimodal-ml in production, what risks or prerequisites should they evaluate first?
pass
AI named pliang279/awesome-multimodal-ml 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 pliang279/awesome-multimodal-ml solve, and who is the primary audience?
pass
AI did not name pliang279/awesome-multimodal-ml — 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)

[![RepoGEO](https://repogeo.com/badge/pliang279/awesome-multimodal-ml.svg)](https://repogeo.com/en/r/pliang279/awesome-multimodal-ml)

HTML

<a href="https://repogeo.com/en/r/pliang279/awesome-multimodal-ml"><img src="https://repogeo.com/badge/pliang279/awesome-multimodal-ml.svg" alt="RepoGEO" /></a>

Pro

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pliang279/awesome-multimodal-ml — 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