REPOGEO REPORT · LITE

Qinying-Liu/Awesome-Open-Vocabulary-Semantic-Segmentation

Default branch main · commit 76c09558 · scanned 6/12/2026, 1:12:21 PM

GitHub: 878 stars · 39 forks

AI VISIBILITY SCORE

17 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 0 warn · 1 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 Qinying-Liu/Awesome-Open-Vocabulary-Semantic-Segmentation, 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

awesome-list, semantic-segmentation, open-vocabulary, zero-shot, weakly-supervised, computer-vision, deep-learning, research-papers, publication-list

highreadme#2

Clarify the README's opening statement to emphasize it's a curated list

Why:

CURRENT

**If you find this project helpful, please consider giving it a star ⭐.**

COPY-PASTE FIX

This repository is a curated and comprehensive list of research papers and resources on Open-Vocabulary Semantic Segmentation and related areas like zero-shot and weakly-supervised methods. It aims to help researchers and practitioners stay updated with the latest advancements. If you find this project helpful, please consider giving it a star ⭐.

mediumlicense#3

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

Choose and add a standard open-source license file (e.g., MIT, Apache-2.0) to the repository root.

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 Qinying-Liu/Awesome-Open-Vocabulary-Semantic-Segmentation

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Grounding DINO

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Grounding DINO · recommended 1×
SAM (Segment Anything Model) · recommended 1×
OWL-ViT (Open-Vocabulary Localization with Vision Transformers) · recommended 1×
CLIPSeg · recommended 1×
SEEM (Segment Everything Everywhere All at Once) · recommended 1×

CATEGORY QUERY
How can I implement open-vocabulary semantic segmentation for various image analysis tasks?
you: not recommended
AI recommended (in order):
1. Grounding DINO
2. SAM (Segment Anything Model)
3. OWL-ViT (Open-Vocabulary Localization with Vision Transformers)
4. CLIPSeg
5. SEEM (Segment Everything Everywhere All at Once)
6. MaskCLIP
AI recommended 6 alternatives but never named Qinying-Liu/Awesome-Open-Vocabulary-Semantic-Segmentation. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the latest research papers on zero-shot or weakly-supervised semantic segmentation?
you: not recommended
AI recommended (in order):
1. SEEM
2. CLIP
AI recommended 2 alternatives but never named Qinying-Liu/Awesome-Open-Vocabulary-Semantic-Segmentation. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
fail
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 Qinying-Liu/Awesome-Open-Vocabulary-Semantic-Segmentation?
pass
AI did not name Qinying-Liu/Awesome-Open-Vocabulary-Semantic-Segmentation — 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 Qinying-Liu/Awesome-Open-Vocabulary-Semantic-Segmentation in production, what risks or prerequisites should they evaluate first?
pass
AI named Qinying-Liu/Awesome-Open-Vocabulary-Semantic-Segmentation 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 Qinying-Liu/Awesome-Open-Vocabulary-Semantic-Segmentation solve, and who is the primary audience?
pass
AI did not name Qinying-Liu/Awesome-Open-Vocabulary-Semantic-Segmentation — 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?

Embed your GEO score

Drop this badge into the README of Qinying-Liu/Awesome-Open-Vocabulary-Semantic-Segmentation. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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Qinying-Liu/Awesome-Open-Vocabulary-Semantic-Segmentation — 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