REPOGEO REPORT · LITE

amusi/ICCV2025-Papers-with-Code

Default branch main · commit 80ac8ca6 · scanned 6/23/2026, 7:37:51 PM

GitHub: 2,881 stars · 256 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 amusi/ICCV2025-Papers-with-Code, 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 with the text of the MIT License.
```

highreadme#2

Explicitly state the repository's purpose as a curated list in the README's opening

Why:

CURRENT

# ICCV 2025 论文和开源项目合集(Papers with Code)

ICCV 2025 Accepance Rate of 24% = 2699 / 11239

COPY-PASTE FIX

# ICCV 2025 论文和开源项目合集(Papers with Code)

这是一个持续更新的ICCV 2025计算机视觉研究论文及其开源项目代码的精选列表。

ICCV 2025 Accepance Rate of 24% = 2699 / 11239

mediumtopics#3

Add topics that describe the repository's function as a collection/list

Why:

CURRENT

artificial-intelligence, computer-vision, iccv, iccv2021, iccv2023, iccv2025, object-detection, semantic-segmentation, transformer

COPY-PASTE FIX

artificial-intelligence, computer-vision, iccv, iccv2021, iccv2023, iccv2025, object-detection, semantic-segmentation, transformer, paper-list, conference-papers, research-collection, papers-with-code

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 amusi/ICCV2025-Papers-with-Code

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×
Hugging Face · recommended 1×
transformers · recommended 1×
diffusers · recommended 1×

CATEGORY QUERY
Where can I find the latest computer vision research papers with accompanying open-source code?
you: not recommended
AI recommended (in order):
1. Papers With Code
2. arXiv
3. Hugging Face
4. transformers
5. diffusers
6. GitHub
7. Kaggle
8. Towards Data Science
9. Medium
AI recommended 9 alternatives but never named amusi/ICCV2025-Papers-with-Code. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for recent breakthroughs and open-source implementations in advanced computer vision topics.
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. 🤗 Diffusers
3. 🤗 Transformers Vision Models
4. PyTorch
5. torchvision
6. PyTorch Lightning
7. OpenMMLab
8. MMDetection
9. MMSegmentation
10. MMDetection3D
11. MMTracking
12. Detectron2
13. TensorFlow
14. Keras
15. TensorFlow Hub
16. OpenCV
17. OpenCV-DNN
AI recommended 17 alternatives but never named amusi/ICCV2025-Papers-with-Code. 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 amusi/ICCV2025-Papers-with-Code?
pass
AI did not name amusi/ICCV2025-Papers-with-Code — 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 amusi/ICCV2025-Papers-with-Code in production, what risks or prerequisites should they evaluate first?
pass
AI named amusi/ICCV2025-Papers-with-Code 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 amusi/ICCV2025-Papers-with-Code solve, and who is the primary audience?
pass
AI did not name amusi/ICCV2025-Papers-with-Code — 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 amusi/ICCV2025-Papers-with-Code. 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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amusi/ICCV2025-Papers-with-Code — 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