REPOGEO REPORT · LITE

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

Default branch v1.0 · commit 7b2e76db · scanned 5/11/2026, 9:12:52 AM

GitHub: 4,781 stars · 458 forks

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 relevant topics to the repository

Why:

COPY-PASTE FIX

['image-segmentation', 'multimodal-ai', 'computer-vision', 'deep-learning', 'interactive-segmentation', 'zero-shot-segmentation', 'neurips-2023', 'segment-anything-model', 'seem']

highreadme#2

Reposition the README's opening to clearly state the repo's purpose as the official SEEM implementation

Why:

CURRENT

# 👀*SEEM:* Segment Everything Everywhere All at Once

:grapes: [Read our arXiv Paper] &nbsp; :apple: [Try our Demo] 

We introduce **SEEM** that can **S**egment **E**verything Everywhere 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!

by Xueyan Zou*, Jianwei Yang*, Hao Zhang*,  Feng Li*, Linjie Li, Jianfeng Wang, Lijuan Wang, Jianfeng Gao^, Yong Jae Lee^, in **NeurIPS 2023**.

COPY-PASTE FIX

# 👀*SEEM:* Segment Everything Everywhere All at Once

This repository is the official implementation of **SEEM**, a unified model for **S**egmenting **E**verything **E**verywhere with **M**ulti-modal prompts all at once, as presented in our **NeurIPS 2023** paper. SEEM enables users to easily segment images using diverse prompts including visual (points, marks, boxes, scribbles, image segments) and language (text, audio), supporting any combination or generalization to custom prompts.

:grapes: [Read our arXiv Paper] &nbsp; :apple: [Try our Demo]

mediumhomepage#3
Add a homepage URL to the repository
Why:
COPY-PASTE FIX
```
https://[YOUR_SEEM_PROJECT_PAGE_OR_DEMO_URL_HERE]
```

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

Of all named tools, what % are you?

Top rival

facebookresearch/segment-anything

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

facebookresearch/segment-anything · recommended 2×
opencv/opencv · recommended 2×
IDEA-Research/Grounded-Segment-Anything · recommended 1×
xdecoder/X-Decoder · recommended 1×
luca-medeiros/lang-segment-anything · recommended 1×

CATEGORY QUERY
What tools allow interactive image segmentation using various visual and language prompts?
you: not recommended
AI recommended (in order):
1. Segment Anything Model (SAM) (facebookresearch/segment-anything)
2. Grounded-SAM (IDEA-Research/Grounded-Segment-Anything)
3. SEEM (Segment Everything Everywhere All at Once) (xdecoder/X-Decoder)
4. Lang-SAM (luca-medeiros/lang-segment-anything)
5. CLIPSeg (timothyliming/CLIPSeg)
6. OpenCV (opencv/opencv)
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 to integrate advanced interactive segmentation capabilities into a multimodal AI image editing application?
you: not recommended
AI recommended (in order):
1. Segment Anything Model (SAM) (facebookresearch/segment-anything)
2. YOLO (You Only Look Once) with Segmentation
3. Detectron2 (facebookresearch/detectron2)
4. MONAI (Medical Open Network for AI) (Project-MONAI/MONAI)
5. OpenCV (opencv/opencv)
6. Hugging Face Transformers (huggingface/transformers)
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

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