REPOGEO REPORT · LITE

Yangzhangcst/Transformer-in-Computer-Vision

Default branch main · commit 12aae994 · scanned 6/27/2026, 1:42:32 PM

GitHub: 1,453 stars · 154 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)

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

AI VISIBILITY SCORE

28 /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

2 / 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 Yangzhangcst/Transformer-in-Computer-Vision, 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

Clarify repo's nature as a curated paper list, not code implementations

Why:

CURRENT

A paper list of some recent Transformer-based CV works.

COPY-PASTE FIX

This repository is a comprehensive, curated list of recent research papers on Transformer-based Computer Vision (CV) works. It serves as a central resource for researchers and practitioners to explore advancements in the field, providing links to original papers and *external* code repositories where available, but does not host implementations directly.

mediumlicense#2

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

Create a LICENSE file in the repository root, for example, using the MIT License, to clearly state the terms of use for the content.

mediumhomepage#3

Set the repository's homepage URL

Why:

COPY-PASTE FIX

Set the repository's 'Homepage' field in GitHub settings to `https://github.com/Yangzhangcst/Transformer-in-Computer-Vision` to provide a direct link to the project's primary location.

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 Yangzhangcst/Transformer-in-Computer-Vision

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

ViT

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

ViT · recommended 1×
Swin Transformer · recommended 1×
DETR · recommended 1×
MAE · recommended 1×
DINO · recommended 1×

CATEGORY QUERY
What are the latest research papers on transformer models for various computer vision problems?
you: not recommended
AI recommended (in order):
1. ViT
2. Swin Transformer
3. DETR
4. MAE
5. DINO
6. ConvNeXt
7. SAM
AI recommended 7 alternatives but never named Yangzhangcst/Transformer-in-Computer-Vision. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find a curated list of recent advancements in vision transformer architectures?
you: not recommended
AI recommended (in order):
1. Papers With Code
2. arXiv
3. GitHub
4. Hugging Face Transformers Library
5. Towards Data Science
6. Medium
7. Google Scholar
AI recommended 7 alternatives but never named Yangzhangcst/Transformer-in-Computer-Vision. 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 Yangzhangcst/Transformer-in-Computer-Vision?
pass
AI named Yangzhangcst/Transformer-in-Computer-Vision explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts Yangzhangcst/Transformer-in-Computer-Vision in production, what risks or prerequisites should they evaluate first?
pass
AI named Yangzhangcst/Transformer-in-Computer-Vision 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 Yangzhangcst/Transformer-in-Computer-Vision solve, and who is the primary audience?
pass
AI did not name Yangzhangcst/Transformer-in-Computer-Vision — 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 Yangzhangcst/Transformer-in-Computer-Vision. 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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Yangzhangcst/Transformer-in-Computer-Vision — 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