RRepoGEO

REPOGEO REPORT · LITE

mbzuai-oryx/Video-ChatGPT

Default branch main · commit 38c7475f · scanned 6/28/2026, 2:43:05 PM

GitHub: 1,503 stars · 130 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)

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

AI VISIBILITY SCORE
71 /100
Needs work
Category recall
1 / 2
Avg rank #2.0 when recommended
Rule findings
2 pass · 0 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 mbzuai-oryx/Video-ChatGPT, 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
    Add a concise project summary immediately after the main title

    Why:

    COPY-PASTE FIX
    Video-ChatGPT is an open-source research model for video conversation, combining LLMs with a visual encoder for spatiotemporal video understanding. It enables meaningful dialogue about video content and includes a rigorous 'Quantitative Evaluation Benchmarking' for video-based conversational models.
  • mediumtopics#2
    Refine and expand repository topics for better categorization

    Why:

    CURRENT
    chatbot, clip, gpt-4, llama, llava, mulit-modal, vicuna, video-chatboat, video-conversation, vision-language, vision-language-pretraining
    COPY-PASTE FIX
    ai-research, benchmarking, chatbot, clip, conversational-ai, gpt-4, llama, llava, multimodal, multimodal-llm, vicuna, video-chatbot, video-conversation, video-llm, video-understanding, vision-language, vision-language-pretraining
  • lowabout#3
    Slightly rephrase the repository description to emphasize its 'open-source research model' nature

    Why:

    CURRENT
    [ACL 2024 🔥] Video-ChatGPT is a video conversation model capable of generating meaningful conversation about videos. It combines the capabilities of LLMs with a pretrained visual encoder adapted for spatiotemporal video representation. We also introduce a rigorous 'Quantitative Evaluation Benchmarking' for video-based conversational models.
    COPY-PASTE FIX
    [ACL 2024 🔥] Video-ChatGPT is an open-source research model for video conversation, combining LLMs with a visual encoder for spatiotemporal video understanding. It enables meaningful dialogue about video content and introduces a rigorous 'Quantitative Evaluation Benchmarking' for video-based conversational models.

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
1 / 2
50% of queries surface mbzuai-oryx/Video-ChatGPT
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
6%
Of all named tools, what % are you?
Top rival
Google Cloud Video AI
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Video AI · recommended 1×
  2. AWS Rekognition Video · recommended 1×
  3. opencv/opencv · recommended 1×
  4. tensorflow/tensorflow · recommended 1×
  5. pytorch/pytorch · recommended 1×
  • CATEGORY QUERY
    How can I build a system for AI to understand video content and generate conversational responses?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Video AI
    2. AWS Rekognition Video
    3. OpenCV (opencv/opencv)
    4. TensorFlow (tensorflow/tensorflow)
    5. PyTorch (pytorch/pytorch)
    6. Google Cloud Speech-to-Text
    7. AWS Transcribe
    8. AssemblyAI
    9. Google Dialogflow CX
    10. Rasa (RasaHQ/rasa)
    11. OpenAI GPT-4
    12. GPT-3.5 Turbo

    AI recommended 12 alternatives but never named mbzuai-oryx/Video-ChatGPT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source models combine large language models with video for multimodal understanding?
    you: #2
    AI recommended (in order):
    1. Video-LLaMA
    2. Video-ChatGPT ← you
    3. InternVideo2
    4. LLaVA-Video
    5. MiniGPT4-Video
    6. OpenFlamingo
    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 mbzuai-oryx/Video-ChatGPT?
    pass
    AI named mbzuai-oryx/Video-ChatGPT explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts mbzuai-oryx/Video-ChatGPT in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mbzuai-oryx/Video-ChatGPT 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 mbzuai-oryx/Video-ChatGPT solve, and who is the primary audience?
    pass
    AI named mbzuai-oryx/Video-ChatGPT explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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mbzuai-oryx/Video-ChatGPT — 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