RRepoGEO

REPOGEO REPORT · LITE

UX-Decoder/Segment-Everything-Everywhere-All-At-Once

Default branch v1.0 · commit 7b2e76db · scanned 6/21/2026, 1:27:16 PM

GitHub: 4,791 stars · 455 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
22 /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
1 / 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 UX-Decoder/Segment-Everything-Everywhere-All-At-Once, 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
  • hightopics#1
    Add comprehensive topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    image-segmentation, multi-modal-ai, computer-vision, deep-learning, neurips-2023, interactive-segmentation, promptable-ai, foundation-model, seem
  • highreadme#2
    Explicitly state this repository's role as the official SEEM implementation

    Why:

    CURRENT
    :grapes: [Read our arXiv Paper]   :apple: [Try our Demo]
    COPY-PASTE FIX
    This is the official repository for **SEEM** (Segment Everything Everywhere All at Once), a NeurIPS 2023 paper. :grapes: [Read our arXiv Paper]   :apple: [Try our Demo]
  • mediumreadme#3
    Enhance the README introduction with integration potential

    Why:

    CURRENT
    We introduce **SEEM** that can **S**egment **E**verything **E**verywhere with **M**ulti-modal prompts all at once. SEEM allows users to easily segment an image using prompts of different types including visual prompts (points, marks, boxes, scribbles and image segments) and language prompts (text and audio), etc. It can also work with any combination of prompts or generalize to custom prompts!
    COPY-PASTE FIX
    We introduce **SEEM** that can **S**egment **E**verything **E**verywhere with **M**ulti-modal prompts all at once. SEEM allows users to easily segment an image using prompts of different types including visual prompts (points, marks, boxes, scribbles and image segments) and language prompts (text and audio), etc. It can also work with any combination of prompts or generalize to custom prompts! **This makes SEEM an ideal foundation for building advanced visual AI assistants and interactive image editing tools.**

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 UX-Decoder/Segment-Everything-Everywhere-All-At-Once
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Grounding DINO · recommended 1×
  2. SAM (Segment Anything Model) · recommended 1×
  3. SEEM (Segment Everything Everywhere All at Once) · recommended 1×
  4. OWL-ViT (Open-World Localization with Vision Transformers) · recommended 1×
  5. GLIP (Grounded Language-Image Pre-training) · recommended 1×
  • CATEGORY QUERY
    What are the best image segmentation models that accept both text and visual prompts?
    you: not recommended
    AI recommended (in order):
    1. Grounding DINO
    2. SAM (Segment Anything Model)
    3. SEEM (Segment Everything Everywhere All at Once)
    4. OWL-ViT (Open-World Localization with Vision Transformers)
    5. GLIP (Grounded Language-Image Pre-training)
    6. Mask2Former

    AI recommended 6 alternatives but never named UX-Decoder/Segment-Everything-Everywhere-All-At-Once. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I integrate advanced interactive image segmentation into a visual AI assistant?
    you: not recommended
    AI recommended (in order):
    1. Segment Anything Model (SAM)
    2. YOLO (You Only Look Once) with Segmentation
    3. Detectron2
    4. OpenCV
    5. MONAI (Medical Open Network for AI)
    6. Hugging Face Transformers
    7. TensorFlow.js
    8. PyTorch Live

    AI recommended 8 alternatives but never named UX-Decoder/Segment-Everything-Everywhere-All-At-Once. 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 UX-Decoder/Segment-Everything-Everywhere-All-At-Once?
    pass
    AI did not name UX-Decoder/Segment-Everything-Everywhere-All-At-Once — 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 UX-Decoder/Segment-Everything-Everywhere-All-At-Once in production, what risks or prerequisites should they evaluate first?
    pass
    AI named UX-Decoder/Segment-Everything-Everywhere-All-At-Once 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 UX-Decoder/Segment-Everything-Everywhere-All-At-Once solve, and who is the primary audience?
    pass
    AI did not name UX-Decoder/Segment-Everything-Everywhere-All-At-Once — 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?

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UX-Decoder/Segment-Everything-Everywhere-All-At-Once — 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