REPOGEO REPORT · LITE

Vision-CAIR/MiniGPT4-video

Default branch main · commit 596adcb0 · scanned 5/31/2026, 9:18:11 AM

GitHub: 639 stars · 70 forks

AI VISIBILITY SCORE

33 /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

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 Vision-CAIR/MiniGPT4-video, 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 opening to emphasize multimodal LLM nature

Why:

CURRENT

# [ECCV 2024 Accepted]Goldfish: Vision-Language Understanding of Arbitrarily Long Videos
# [CVPR2024W]MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens
**This repo contains the codes for MiniGPT4-video for short video understanding and Goldfish for long video understanding.**

COPY-PASTE FIX

This repository presents Goldfish and MiniGPT4-video, two state-of-the-art multimodal Large Language Models (LLMs) designed for comprehensive video understanding, covering both arbitrarily long and short video content. Goldfish (ECCV 2024 Accepted) focuses on long videos, while MiniGPT4-video (CVPR2024W) advances multimodal LLMs for short video understanding with interleaved visual-textual tokens.

mediumtopics#2

Add specific multimodal LLM topics

Why:

CURRENT

long-video-understanding, video-question-answering, video-retrieval, video-understanding

COPY-PASTE FIX

long-video-understanding, video-question-answering, video-retrieval, video-understanding, multimodal-llm, large-language-models, video-llm, vision-language-model

lowhomepage#3
Update homepage to reflect MiniGPT4-video project page
Why:
CURRENT
```
https://vision-cair.github.io/Goldfish_website/
```
COPY-PASTE FIX
```
https://vision-cair.github.io/MiniGPT4-video/
```

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 Vision-CAIR/MiniGPT4-video

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

FFmpeg

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

FFmpeg · recommended 2×
OpenCV · recommended 2×
PyTorch · recommended 2×
TensorFlow · recommended 2×
Hugging Face Transformers · recommended 1×

CATEGORY QUERY
How can I build a system for comprehensive understanding and analysis of very long video content?
you: not recommended
AI recommended (in order):
1. FFmpeg
2. OpenCV
3. Hugging Face Transformers
4. Whisper
5. GPT-4
6. OpenAI API
7. Claude
8. Anthropic API
9. Llama 3
10. CLIP
11. BLIP
12. PyTorch
13. TensorFlow
14. Elasticsearch
15. Apache Kafka
16. Grafana
17. Kibana
AI recommended 17 alternatives but never named Vision-CAIR/MiniGPT4-video. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools help develop AI models for question answering based on video content?
you: not recommended
AI recommended (in order):
1. PyTorch
2. TensorFlow
3. Transformers (Hugging Face)
4. OpenCV
5. MMAction2
6. Detectron2
7. FAISS
8. SpaCy
9. NLTK
10. FFmpeg
AI recommended 10 alternatives but never named Vision-CAIR/MiniGPT4-video. 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 Vision-CAIR/MiniGPT4-video?
pass
AI did not name Vision-CAIR/MiniGPT4-video — 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 Vision-CAIR/MiniGPT4-video in production, what risks or prerequisites should they evaluate first?
pass
AI named Vision-CAIR/MiniGPT4-video 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 Vision-CAIR/MiniGPT4-video solve, and who is the primary audience?
pass
AI named Vision-CAIR/MiniGPT4-video explicitly
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 Vision-CAIR/MiniGPT4-video. 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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HTML

<a href="https://repogeo.com/en/r/Vision-CAIR/MiniGPT4-video"><img src="https://repogeo.com/badge/Vision-CAIR/MiniGPT4-video.svg" alt="RepoGEO" /></a>

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Vision-CAIR/MiniGPT4-video — 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