Tiancheng Gu*, Kaicheng Yang*, Ziyong Feng, Xingjun Wang, Yanzhao Zhang, Dingkun Long, Yingda Chen, Weidong Cai, Jiankang Deng
π‘ Project Page | π Paper | UniME(Phi3.5-V-4.2B) π€/π€ | UniME(LLaVA-v1.6-7B)π€/π€ | UniME(LLaVA-OneVision-7B)π€/π€
UniME achieves the top ranking on the MMEB leaderboard training using only 336Γ336 image resolution.οΌThe screenshot was captured at 08:00 UTC+8 on May 6, 2025.οΌ
- [2025/05/06]: β¨We release the model weight of UniME-LLaVA-OneVision-7B in π€ Huggingface, which achieves the top ranking on the MMEB leaderboard.
- [2025/04/24]: β¨We release the evaluate and demo code.
- [2025/04/24]: β¨The paper of UniME is submitted to arxiv.
- [2025/04/22]: β¨We release the model weight of UniME in π€ Huggingface
To enhance the MLLM's embedding capability, we propose textual discriminative knowledge distillation. The training process involves decoupling the MLLM's LLM component and processing text with the prompt "Summarize the above sentences in one word.", followed by aligning the student (MLLM) and teacher (NV-Embed V2) embeddings via KL divergence on batch-wise similarity distributions. Notably, only the LLM component is fine-tuned during this process, while all other parameters remain frozen.
After that, we propose hard negative enhanced instruction tuning enhances multimodal systems by improving visual sensitivity, strengthening cross-modal alignment, and boosting instruction-following capabilities. At its core are two key innovations: a false negative filtering mechanism using a similarity threshold to eliminate misleading samples, and an automatic hard negative sampling strategy that selects top-k similar but non-matching examples to increase training difficulty.
conda create -n uniME python=3.10 -y
conda activate uniME
pip install -r requirements.txtgit clone https://github.com/deepglint/UniME.git
cd UniMEimport torch
from PIL import Image
from torch.nn import functional as F
from self_evaluate.utils.utils import init_model_and_transform
model_name = "phi35V"
base_model_path="DeepGlint-AI/UniME-Phi3.5-V-4.2B"
# model_name = "llava_16"
# base_model_path="DeepGlint-AI/UniME-LLaVA-1.6-7B"
if model_name == "phi35V":
img_prompt = '<|user|>\n<|image_1|>\nSummary above image in one word: <|end|>\n<|assistant|>\n'
text_prompt = '<|user|>\n<sent>\nSummary above sentence in one word: <|end|>\n<|assistant|>\n'
elif model_name == "llava_16":
img_prompt = "[INST] <image>\nSummary above image in one word: [/INST]"
text_prompt = "[INST] <sent>\nSummary above sentence in one word: [/INST]"
text = "A man is crossing the street with a red car parked nearby."
image_path = "figures/demo.png"
input_texts = text_prompt.replace('<sent>', text)
input_image_prompt = img_prompt
input_image = [Image.open(image_path)]
model, transform = init_model_and_transform(model_name, base_model_path)
inputs_text = transform(text=input_texts,
images=None,
return_tensors="pt",
padding=True)
for key in inputs_text: inputs_text[key] = inputs_text[key].to("cuda")
inputs_image = transform(text=input_image_prompt,
images=input_image,
return_tensors="pt",
padding=True).to("cuda")
with torch.no_grad():
emb_text = model(**inputs_text, output_hidden_states=True, return_dict=True).hidden_states[-1][:, -1, :]
emb_image = model(**inputs_image, output_hidden_states=True, return_dict=True).hidden_states[-1][:, -1, :]
emb_text = F.normalize(emb_text, dim=-1)
emb_image = F.normalize(emb_image, dim=-1)
Score = emb_image @ emb_text.T
print("Score: ", Score) # Score: 0.591.Please prepare data follow Flickr, CoCo, ShareGPT4V, Urban1k, SugarCrepe
2.Edit data path in "self_evaluate/utils/data_path.py".
# Phi35V
bash shell/test_Phi35V.sh
# LLaVA1.6-mistral
bash shell/test_LLaVA16.sh1.Follow MMEB
git clone https://github.com/TIGER-AI-Lab/VLM2Vec.git
cd VLM2Vec
wget https://huggingface.co/datasets/TIGER-Lab/MMEB-eval/resolve/main/images.zip
unzip images.zip -d eval_images/cd VLM2Vec
#! Don't forget to edit data path
##### Phi35v
mkdir ../evaluate_results/uniME_phi35V/
python eval.py --model_name ../models/uniME_phi35V \
--model_backbone phi3_v \
--encode_output_path ../evaluate_results/uniME_phi35V/ \
--num_crops 4 --max_len 256 \
--pooling last --normalize True \
--dataset_name TIGER-Lab/MMEB-eval \
--subset_name N24News CIFAR-100 HatefulMemes VOC2007 SUN397 ImageNet-A ImageNet-R ObjectNet Country211 \
--dataset_split test --per_device_eval_batch_size 16 \
--image_dir eval_images/
##### llava16
mkdir ../evaluate_results/uniME_llava_16/
python eval.py --model_name ../models/uniME_llava_16 \
--model_backbone llava_next \
--encode_output_path ../evaluate_results/uniME_llava_16/ \
--num_crops 4 --max_len 4096 \
--pooling last --normalize True \
--dataset_name TIGER-Lab/MMEB-eval \
--subset_name N24News CIFAR-100 HatefulMemes VOC2007 SUN397 ImageNet-A ImageNet-R ObjectNet Country211 \
--dataset_split test --per_device_eval_batch_size 16 \
--image_dir eval_images/ Many thanks to the code bases from E5-V, VLM2VEC, LamRA, ms-swift, sugar-crepe, LongCLIP, tevatron.
If you find this repository useful, please use the following BibTeX entry for citation.
@misc{gu2025breakingmodalitybarrieruniversal,
title={Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMs},
author={Tiancheng Gu and Kaicheng Yang and Ziyong Feng and Xingjun Wang and Yanzhao Zhang and Dingkun Long and Yingda Chen and Weidong Cai and Jiankang Deng},
year={2025},
eprint={2504.17432},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2504.17432},
}