RRepoGEO

REPOGEO REPORT · LITE

bytedance/Sa2VA

Default branch main · commit 20aa0b6c · scanned 6/19/2026, 3:57:28 PM

GitHub: 1,614 stars · 118 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
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 bytedance/Sa2VA, 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
  • highabout#1
    Update the repository description to clearly state its core purpose

    Why:

    CURRENT
    Official Repo For Pixel-LLM Codebase: Sa2VA (Arxiv-25), SAMTok (CVPR-26), VRT, SaSaSa2VA (1-st solution for LSVOS)
    COPY-PASTE FIX
    Official codebase for Pixel LLMs, featuring Sa2VA: a unified model marrying SAM-2 with LLaVA for pixel-level grounded understanding in multimodal LLMs, supporting referring segmentation, visual prompting, and image/video chat.
  • mediumreadme#2
    Add a comparison section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with Alternatives

[Add a section here discussing how Sa2VA integrates or extends capabilities found in models like SAM, LLaVA, or Grounding DINO, highlighting its unique contributions to pixel-level grounded understanding.]
  • lowhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://sa2va.github.io/ (or the official project page for Sa2VA)

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 bytedance/Sa2VA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Grounding DINO
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Grounding DINO · recommended 2×
  2. SAM (Segment Anything Model) · recommended 2×
  3. SEEM (Segment Everything Everywhere All at Once) · recommended 2×
  4. Mask2Former · recommended 2×
  5. LLaVA (Large Language and Vision Assistant) · recommended 1×
  • CATEGORY QUERY
    How to achieve pixel-level grounded understanding in multimodal large language models for image and video chat?
    you: not recommended
    AI recommended (in order):
    1. Grounding DINO
    2. SAM (Segment Anything Model)
    3. LLaVA (Large Language and Vision Assistant)
    4. GPT-4V (GPT-4 with Vision)
    5. OWL-ViT (Open-World Localization Vision Transformer)
    6. SEEM (Segment Everything Everywhere All at Once)
    7. Mask2Former
    8. Mask R-CNN
    9. Swin Transformer
    10. InternImage-H

    AI recommended 10 alternatives but never named bytedance/Sa2VA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source models combine vision foundation models with LLMs for referring segmentation and visual prompting?
    you: not recommended
    AI recommended (in order):
    1. Grounding DINO
    2. SAM (Segment Anything Model)
    3. LLaVA
    4. GPT-4V
    5. OWL-ViT
    6. SEEM (Segment Everything Everywhere All at Once)
    7. OneFormer
    8. Mask2Former

    AI recommended 8 alternatives but never named bytedance/Sa2VA. 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 bytedance/Sa2VA?
    pass
    AI named bytedance/Sa2VA explicitly

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

  • If a team adopts bytedance/Sa2VA in production, what risks or prerequisites should they evaluate first?
    pass
    AI named bytedance/Sa2VA 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 bytedance/Sa2VA solve, and who is the primary audience?
    pass
    AI named bytedance/Sa2VA explicitly

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

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bytedance/Sa2VA — 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