UniME-V2-Qwen2VL-7B

<div align="center"> <h1>UniME-V2: MLLM-as-a-Judge for Universal Multimodal Embedding Learning</h1>

<a href="https://scholar.google.com/citations?hl=zh-CN&user=9etrpbYAAAAJ">Tiancheng Gu*</a>,</span> <a href="https://kaicheng-yang0828.github.io">Kaicheng Yang*</a>,</span> <a href="https://kcz358.github.io/">Kaichen Zhang</a>,</span> <a href="https://scholar.google.com/citations?hl=zh-CN&user=1ckaPgwAAAAJ">Xiang An</a>,</span> Ziyong Feng,</span>
<a href="https://scholar.google.com/citations?hl=en&user=LatWlFAAAAAJ">Yueyi Zhang</a>,</span> <a href="https://weidong-tom-cai.github.io">Weidong Cai</a>,</span> <a href="https://jiankangdeng.github.io">Jiankang Deng</a>,</span> <a href="https://lidongbing.github.io">Lidong Bing</a></span>

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💡 Highlights

• We introduce an MLLM-as-a-Judge pipeline for hard negative mining that uses the advanced understanding capabilities of MLLM to assess the semantic alignment of each query-candidate pair within a globally retrieved potential hard negative set.

<div align="center"> <img src="Figures/method1.jpg" width="95%"> </div>

• We present UniME-V2, a novel universal multimodal embedding model trained with an MLLM judgment based distribution alignment framework. By leveraging semantic matching scores as soft labels, the model effectively captures semantic differences between candidates, significantly enhancing its discriminative capability. Meanwhile, we propose UniME-V2-Reranker, a reranking model trained on high-quality, diverse hard negatives through a joint pairwise and listwise optimization approach.

<div align="center"> <img src="Figures/method2.jpg" width="60%"> </div>

🛠️ Implementation

🚀 Quick Start

git clone https://github.com/deepglint/UniME-v2.git
cd UniME-v2

conda create -n uniMEv2 python=3.10 -y
conda activate uniMEv2
pip install -r requirements.txt

# Optional: Install Flash Attention for acceleration
# wget https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.4cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
# pip install flash_attn-2.7.4.post1+cu12torch2.4cxx11abiFALSE-cp310-cp310-linux_x86_64.whl

🔍 Embedding model & Rerank model

import torch
from torch.nn import functional as F
from utils.utils import init_model_and_processor, prepare_stage_data, parse_answer_index

device="cuda"
embedding=False # adjust embedding model or rerank model
if embedding:
    model_name="models/UniME-V2_qwen2VL_2B"
    # model_name="models/UniME-V2_qwen2VL_7B"
    # model_name="models/UniME-V2_LLaVA_onevision_8B"
    text = "A man is crossing the street with a red car parked nearby."
    image_path = "Figures/demo.png"
else:
    model_name="models/UniME-v2-rerank_qwen25VL_7B"
    text = ["A man is crossing the street with a red car parked nearby.",  #! Target text
            "A woman is walking her dog with a blue bicycle leaning nearby.",
            "A child is riding a scooter past a green truck stopped nearby.",
            "A couple is waiting for the bus beside a yellow taxi parked nearby.",
            "A jogger is running along the path with a black motorcycle parked nearby."]
    image_path = "Figures/demo.png"

model, processor = init_model_and_processor(model_name, device, embedding=embedding)

if embedding:
    inputs_image, inputs_txt = prepare_stage_data(model_name, processor, text, image_path, embedding=embedding)
    inputs_image = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in inputs_image.items()}
    inputs_txt = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in inputs_txt.items()}
    with torch.no_grad():
        emb_text = model(**inputs_txt, 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.item()) # qwen2VL 2B : Score: 0.62109375
else:
    inputs = prepare_stage_data(model_name, processor, text, image_path, embedding=embedding)
    inputs = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
    with torch.no_grad():
        generated_ids = model.generate(**inputs, max_new_tokens=128, output_scores=True, return_dict_in_generate=True, do_sample=False).sequences
    generated_ids_trimmed = [
        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs['input_ids'], generated_ids)
    ]
    output_text = processor.batch_decode(
        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
    )
    print("Rerank Answer: ", parse_answer_index(output_text[0])) # qwen25VL 7B: Rerank Answer: 0

📊 Results

🌈 Diversity Retrieval

<div align="center"> <img src="Figures/UniME_v2_diversity_retrieval.png" width="90%"> </div>

🏆 MMEB

<div align="center"> <img src="Figures/UniME_v2_MMEB.png" width="90%"> </div>

💬 Support

🖊️ Citation

If you find this repository useful, please use the following BibTeX entry for citation.

@misc{gu2025unimev2mllmasajudgeuniversalmultimodal,
      title={UniME-V2: MLLM-as-a-Judge for Universal Multimodal Embedding Learning}, 
      author={Tiancheng Gu and Kaicheng Yang and Kaichen Zhang and Xiang An and Ziyong Feng and Yueyi Zhang and Weidong Cai and Jiankang Deng and Lidong Bing},
      year={2025},
      eprint={2510.13515},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2510.13515}, 
}

@inproceedings{unime,
      title={Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMs},
      author={Gu, Tiancheng and Yang, Kaicheng and Feng, Ziyong and Wang, Xingjun and Zhang, Yanzhao and Long, Dingkun and Chen, Yingda and Cai, Weidong and Deng, Jiankang},
      booktitle={ACM MM},
      year={2025}
}

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