REPOGEO REPORT · LITE

DAMO-NLP-SG/Video-LLaMA

Default branch main · commit 64888c0a · scanned 6/24/2026, 1:27:40 PM

GitHub: 3,143 stars · 287 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

35 /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

3 / 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 DAMO-NLP-SG/Video-LLaMA, 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

Clarify the project's core identity as an instruction-tuned multimodal LLM in the README's opening

Why:

CURRENT

This is the repo for the Video-LLaMA project, which is working on empowering large language models with video and audio understanding capabilities.

COPY-PASTE FIX

Video-LLaMA is an instruction-tuned audio-visual large language model (LLM) designed for comprehensive video understanding and interaction.

highhomepage#2
Add the project's homepage URL
Why:
COPY-PASTE FIX
```
https://arxiv.org/abs/2306.02858
```

mediumtopics#3

Add more specific topics to emphasize instruction-tuning and multimodal LLM capabilities

Why:

CURRENT

blip2, cross-modal-pretraining, large-language-models, llama, minigpt4, multi-modal-chatgpt, video-language-pretraining, vision-language-pretraining

COPY-PASTE FIX

blip2, cross-modal-pretraining, large-language-models, llama, minigpt4, multi-modal-chatgpt, video-language-pretraining, vision-language-pretraining, instruction-tuned-llm, multimodal-llm, video-llm, audio-llm

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 DAMO-NLP-SG/Video-LLaMA

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenAI CLIP

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

OpenAI CLIP · recommended 1×
Google's VGGish · recommended 1×
OpenAI's Jukebox · recommended 1×
Facebook's Wav2Vec 2.0 · recommended 1×
BERT · recommended 1×

CATEGORY QUERY
How can I empower a language model to understand both video and audio content?
you: not recommended
AI recommended (in order):
1. OpenAI CLIP
2. Google's VGGish
3. OpenAI's Jukebox
4. Facebook's Wav2Vec 2.0
5. BERT
6. RoBERTa
7. GPT-3/4
8. Google's Perceiver IO
9. Meta's Data2Vec
10. Microsoft's Florence
11. Florence-2
12. Hugging Face Transformers Library
13. ViT (Vision Transformer)
14. HuBERT
15. T5
16. BART
17. PyTorchVideo
18. SlowFast
19. X3D
20. torchaudio
21. LongT5
22. DeepMind's Gato
AI recommended 22 alternatives but never named DAMO-NLP-SG/Video-LLaMA. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are effective instruction-tuned models for comprehensive video understanding and interaction?
you: not recommended
AI recommended (in order):
1. GPT-4o
2. Google Gemini
3. Video-LLaVA (PKU-YuanGroup/Video-LLaVA)
4. InternVideo2 (OpenGVLab/InternVideo2)
5. OpenAI's Sora
6. Microsoft's Florence-2 (microsoft/Florence)
AI recommended 6 alternatives but never named DAMO-NLP-SG/Video-LLaMA. 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 DAMO-NLP-SG/Video-LLaMA?
pass
AI named DAMO-NLP-SG/Video-LLaMA explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts DAMO-NLP-SG/Video-LLaMA in production, what risks or prerequisites should they evaluate first?
pass
AI named DAMO-NLP-SG/Video-LLaMA 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 DAMO-NLP-SG/Video-LLaMA solve, and who is the primary audience?
pass
AI named DAMO-NLP-SG/Video-LLaMA 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 DAMO-NLP-SG/Video-LLaMA. 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/DAMO-NLP-SG/Video-LLaMA"><img src="https://repogeo.com/badge/DAMO-NLP-SG/Video-LLaMA.svg" alt="RepoGEO" /></a>

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DAMO-NLP-SG/Video-LLaMA — 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