REPOGEO REPORT · LITE

pprp/awesome-attention-mechanism-in-cv

Default branch main · commit 59997fe4 · scanned 5/19/2026, 9:52:40 AM

GitHub: 1,266 stars · 172 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 pprp/awesome-attention-mechanism-in-cv, 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 'awesome-list' to repository topics

Why:

CURRENT

attention-mechanisms, attention-model, computer-vision, implementation, plugandplay, pytorch-attention, vision-transformer

COPY-PASTE FIX

attention-mechanisms, attention-model, computer-vision, implementation, plugandplay, pytorch-attention, vision-transformer, awesome-list

highreadme#2

Refine the README's 'Introduction' paragraph to emphasize its value as a curated list

Why:

CURRENT

This is a list of awesome attention mechanisms used in computer vision, as well as a collection of plug and play modules. Due to limited ability and energy, many modules may not be included. If you have any suggestions or improvements, welcome to submit an issue or PR.

COPY-PASTE FIX

This is a curated and comprehensive list of awesome attention mechanisms and plug-and-play modules specifically for computer vision. It serves as a central resource for researchers and practitioners looking to explore and integrate state-of-the-art attention models into their projects.

mediumhomepage#3
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://github.com/pprp/awesome-attention-mechanism-in-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 pprp/awesome-attention-mechanism-in-cv

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

SENet

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

SENet · recommended 1×
CBAM · recommended 1×
ECA-Net · recommended 1×
GC Block · recommended 1×
NLNet · recommended 1×

CATEGORY QUERY
What are the best plug-and-play attention modules for improving computer vision models?
you: not recommended
AI recommended (in order):
1. SENet
2. CBAM
3. ECA-Net
4. GC Block
5. NLNet
6. CA
AI recommended 6 alternatives but never named pprp/awesome-attention-mechanism-in-cv. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can I integrate effective attention mechanisms into my vision transformer architectures?
you: not recommended
AI recommended (in order):
1. Swin Transformer
2. PVT
3. CoaT
4. DeiT
5. Perceiver IO
6. LocalViT
7. EfficientViT
AI recommended 7 alternatives but never named pprp/awesome-attention-mechanism-in-cv. 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 pprp/awesome-attention-mechanism-in-cv?
pass
AI did not name pprp/awesome-attention-mechanism-in-cv — 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 pprp/awesome-attention-mechanism-in-cv in production, what risks or prerequisites should they evaluate first?
pass
AI named pprp/awesome-attention-mechanism-in-cv 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 pprp/awesome-attention-mechanism-in-cv solve, and who is the primary audience?
pass
AI did not name pprp/awesome-attention-mechanism-in-cv — 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 pprp/awesome-attention-mechanism-in-cv. 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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pprp/awesome-attention-mechanism-in-cv — 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