RRepoGEO

REPOGEO REPORT · LITE

DAMO-NLP-SG/Video-LLaMA

Default branch main · commit 64888c0a · scanned 5/14/2026, 2:18:15 AM

GitHub: 3,145 stars · 287 forks

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
    Reposition the README's opening paragraph to clarify its solution-level purpose

    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 language model that enables AI systems to understand video content and generate natural language responses. It empowers large language models with advanced video and audio comprehension capabilities, making it a ready-to-use solution for complex video analysis tasks.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2306.02858
  • lowreadme#3
    Reinforce 'instruction-tuned' and 'video understanding' in the README's introductory text

    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 language model specifically designed for comprehensive video understanding, empowering large language models with the ability to process and respond to complex video and audio inputs.

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
0%
Of all named tools, what % are you?
Top rival
tensorflow/tensorflow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. tensorflow/tensorflow · recommended 2×
  2. huggingface/transformers · recommended 2×
  3. facebookresearch/fairseq · recommended 2×
  4. pytorch/pytorch · recommended 1×
  5. GPT-3.5 · recommended 1×
  • CATEGORY QUERY
    How can I build an AI that understands video content and generates natural language responses?
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. TensorFlow (tensorflow/tensorflow)
    3. Hugging Face Transformers (huggingface/transformers)
    4. GPT-3.5
    5. GPT-4
    6. BERT (google-research/bert)
    7. T5 (google-research/text-to-text-transfer-transformer)
    8. BART (facebookresearch/fairseq)
    9. OpenCV (opencv/opencv)
    10. MMAction2 (open-mmlab/mmaction2)
    11. Detectron2 (facebookresearch/detectron2)
    12. VideoMAE (facebookresearch/VideoMAE)
    13. ViViT
    14. OpenAI API

    AI recommended 14 alternatives but never named DAMO-NLP-SG/Video-LLaMA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for tools to integrate audio and visual streams into a language model for comprehension.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    3. TensorFlow / Keras (tensorflow/tensorflow)
    4. OpenMMLab (open-mmlab/mmengine)
    5. DeepMind's Perceiver IO / Flamingo
    6. Fairseq (facebookresearch/fairseq)

    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?

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DAMO-NLP-SG/Video-LLaMA — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite