REPOGEO REPORT · LITE

dk-liang/Awesome-Visual-Transformer

Default branch main · commit d7617b3e · scanned 6/27/2026, 2:27:36 PM

GitHub: 3,586 stars · 405 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

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 dk-liang/Awesome-Visual-Transformer, 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

highlicense#1
Add a LICENSE file to the repository
Why:
COPY-PASTE FIX
```
Create a LICENSE file in the repository root, for example, with the MIT License text.
```

highhomepage#2

Add a homepage URL to the repository's 'About' section

Why:

COPY-PASTE FIX

Set the 'Homepage' URL in the repository's 'About' section to a relevant external link (e.g., a project page or related blog post), or to the repository's own GitHub URL if no other dedicated page exists.

highreadme#3

Clarify the README's opening statement to emphasize it's a curated collection of papers

Why:

CURRENT

Collect some Transformer with Computer-Vision (CV) papers.

COPY-PASTE FIX

This repository is a curated and comprehensive collection of research papers, surveys, and technical blogs focused on Visual Transformers and their applications in Computer Vision (CV).

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 dk-liang/Awesome-Visual-Transformer

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Papers With Code

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Papers With Code · recommended 1×
arXiv · recommended 1×
Google Scholar · recommended 1×
Awesome-Vision-Transformers · recommended 1×
Connected Papers · recommended 1×

CATEGORY QUERY
Where can I find a comprehensive list of research papers on visual transformers?
you: not recommended
AI recommended (in order):
1. Papers With Code
2. arXiv
3. Google Scholar
4. Awesome-Vision-Transformers
5. Connected Papers
6. Semantic Scholar
AI recommended 6 alternatives but never named dk-liang/Awesome-Visual-Transformer. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the latest advancements and papers on using transformers for computer vision tasks?
you: not recommended
AI recommended (in order):
1. ViT
2. DeiT
3. Swin Transformer
4. PVT
5. CoAtNet
6. EfficientFormer
7. DETR
8. Deformable DETR
9. Mask2Former
10. SegFormer
11. DAB-DETR
12. MAE
13. BEiT
14. Data2vec
15. CLIP
16. ALIGN
17. Flamingo
18. PaLI
AI recommended 18 alternatives but never named dk-liang/Awesome-Visual-Transformer. 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 dk-liang/Awesome-Visual-Transformer?
pass
AI did not name dk-liang/Awesome-Visual-Transformer — 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 dk-liang/Awesome-Visual-Transformer in production, what risks or prerequisites should they evaluate first?
pass
AI named dk-liang/Awesome-Visual-Transformer 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 dk-liang/Awesome-Visual-Transformer solve, and who is the primary audience?
pass
AI did not name dk-liang/Awesome-Visual-Transformer — 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 dk-liang/Awesome-Visual-Transformer. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/dk-liang/Awesome-Visual-Transformer.svg)](https://repogeo.com/en/r/dk-liang/Awesome-Visual-Transformer)

HTML

<a href="https://repogeo.com/en/r/dk-liang/Awesome-Visual-Transformer"><img src="https://repogeo.com/badge/dk-liang/Awesome-Visual-Transformer.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

dk-liang/Awesome-Visual-Transformer — 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