REPOGEO REPORT · LITE

TIGER-AI-Lab/VLM2Vec

Default branch main · commit 6e1e1d42 · scanned 6/1/2026, 10:27:14 AM

GitHub: 650 stars · 60 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 TIGER-AI-Lab/VLM2Vec, 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

Reposition the README's opening sentence to clarify project type

Why:

CURRENT

This repository contains the official code and data for **VLM2Vec-V2**, a unified framework for learning powerful multimodal embeddings across diverse visual formats including images, videos, and visual documents.

COPY-PASTE FIX

VLM2Vec-V2 is an open-source **research and development framework** for **training and benchmarking** state-of-the-art unified multimodal embeddings across images, videos, and visual documents.

mediumabout#2

Enhance the GitHub repository description to highlight its framework and benchmarking aspects

Why:

CURRENT

This repo contains the code for "VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks" [ICLR 2025]

COPY-PASTE FIX

Research framework for training & benchmarking unified multimodal embeddings (images, videos, documents), featuring the MMEB-V2 benchmark. Official code for ICLR 2025.

lowcomparison#3

Add a 'Comparison' section to the README to differentiate from common pre-trained models

Why:

COPY-PASTE FIX

Add a new section, e.g., 'Why VLM2Vec? (vs. CLIP, PaLM, etc.)', explaining that VLM2Vec is a framework for *developing and evaluating* such models and benchmarks, rather than a pre-trained model itself.

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 TIGER-AI-Lab/VLM2Vec

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

openai/CLIP

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

openai/CLIP · recommended 2×
OpenAI CLIP · recommended 1×
OpenAI DALL-E 3 Embeddings · recommended 1×
Google PaLM · recommended 1×
Google Gemini Embeddings · recommended 1×

CATEGORY QUERY
How to create unified embeddings for images, videos, and documents for retrieval tasks?
you: not recommended
AI recommended (in order):
1. OpenAI CLIP
2. OpenAI DALL-E 3 Embeddings
3. Google PaLM
4. Google Gemini Embeddings
5. Hugging Face Transformers
6. Meta ImageBind
7. Jina AI CLIP-as-service
8. Jina Embeddings
9. Weaviate
10. PyTorch
11. TensorFlow
AI recommended 11 alternatives but never named TIGER-AI-Lab/VLM2Vec. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a framework to learn multimodal representations for efficient RAG systems.
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. 🤗 Diffusers (huggingface/diffusers)
3. CLIP (openai/CLIP)
4. OpenCLIP (mlfoundations/open_clip)
5. OpenAI CLIP (openai/CLIP)
6. Meta AI DINOv2 (facebookresearch/dinov2)
7. DINO (facebookresearch/dino)
8. BERT (google-research/bert)
9. RoBERTa
10. Google Flax (google/flax)
11. JAX (google/jax)
12. Vision Transformer (ViT) (google-research/vision_transformer)
13. PyTorch Lightning (Lightning-AI/lightning)
14. TorchVision (pytorch/vision)
15. TorchText (pytorch/text)
16. Faiss (facebookresearch/faiss)
AI recommended 16 alternatives but never named TIGER-AI-Lab/VLM2Vec. 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 TIGER-AI-Lab/VLM2Vec?
pass
AI named TIGER-AI-Lab/VLM2Vec explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts TIGER-AI-Lab/VLM2Vec in production, what risks or prerequisites should they evaluate first?
pass
AI named TIGER-AI-Lab/VLM2Vec 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 TIGER-AI-Lab/VLM2Vec solve, and who is the primary audience?
pass
AI named TIGER-AI-Lab/VLM2Vec explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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TIGER-AI-Lab/VLM2Vec — 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