REPOGEO REPORT · LITE

360CVGroup/FG-CLIP

Default branch main · commit 28794401 · scanned 6/11/2026, 8:23:08 PM

GitHub: 752 stars · 36 forks

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 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 360CVGroup/FG-CLIP, 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 'bilingual' and 'multilingual' to repository topics

Why:

CURRENT

clip, cross-modal-retrieval, fine-grained-classification, text-image-retrieval

COPY-PASTE FIX

clip, cross-modal-retrieval, fine-grained-classification, text-image-retrieval, bilingual, multilingual

mediumabout#2

Enhance the repository description to highlight bilingual support

Why:

CURRENT

New generation of CLIP with strong fine grained discrimination capability, ICML2026 and ICML2025

COPY-PASTE FIX

New generation of CLIP for superior fine-grained discrimination and robust bilingual (Chinese/English) vision-language alignment. Accepted at ICML2026 and ICML2025.

mediumreadme#3

Refine the README's opening sentence for stronger positioning

Why:

CURRENT

This repository is the official implementation of FG-CLIP and FG-CLIP 2. As a new generation of text-image cross-modal model, it excels in fine-grained understanding. FG-CLIP 2 supports Chinese and English bilingualism, and in 29 datasets and 8 diverse tasks, the model surpasses strong baseline models including SigLIP 2 and MetaCLIP 2, achieving the current best performance in both language tasks.

COPY-PASTE FIX

FG-CLIP 2 is the official implementation of our next-generation vision-language alignment model, uniquely engineered for **superior fine-grained discrimination** and **robust bilingual (Chinese/English) support**. It significantly outperforms general CLIP models and other strong baselines in detailed text-image understanding across both languages.

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 360CVGroup/FG-CLIP

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

CLIP (Contrastive Language-Image Pre-training)

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

CLIP (Contrastive Language-Image Pre-training) · recommended 1×
ALBEF (Align before Fuse) · recommended 1×
BLIP (Bootstrapping Language-Image Pre-training) · recommended 1×
OFA (One-For-All) · recommended 1×
CoCa (Contrastive Captioners) · recommended 1×

CATEGORY QUERY
What are the best models for fine-grained text-image cross-modal retrieval?
you: not recommended
AI recommended (in order):
1. CLIP (Contrastive Language-Image Pre-training)
2. ALBEF (Align before Fuse)
3. BLIP (Bootstrapping Language-Image Pre-training)
4. OFA (One-For-All)
5. CoCa (Contrastive Captioners)
6. FLAVA (A Foundational Language And Vision Alignment Model)
AI recommended 6 alternatives but never named 360CVGroup/FG-CLIP. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a vision-language alignment model with strong bilingual support for fine-grained tasks.
you: not recommended
AI recommended (in order):
1. mPLUG-Owl2
2. BLIP-2
3. X-VLM
4. OpenCLIP
5. mBERT
6. XLM-RoBERTa
7. Flamingo
8. ViLT
AI recommended 8 alternatives but never named 360CVGroup/FG-CLIP. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
pass
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 360CVGroup/FG-CLIP?
pass
AI named 360CVGroup/FG-CLIP explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts 360CVGroup/FG-CLIP in production, what risks or prerequisites should they evaluate first?
pass
AI named 360CVGroup/FG-CLIP 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 360CVGroup/FG-CLIP solve, and who is the primary audience?
pass
AI named 360CVGroup/FG-CLIP explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/360CVGroup/FG-CLIP"><img src="https://repogeo.com/badge/360CVGroup/FG-CLIP.svg" alt="RepoGEO" /></a>

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360CVGroup/FG-CLIP — 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