HugC
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VisME-Qwen25VL-7B

@@ -13,39 +13,39 @@ library_name: transformers
 # VisME (Qwen2.5-VL-7B)
-[English](README-en.md) | 简体中文
-**VisME** 是我们 CVPR 2026 论文 [*Illuminating Visual Identity in Universal Multimodal Embeddings*](https://openaccess.thecvf.com/content/CVPR2026/html/Cao_Illuminating_Visual_Identity_in_Universal_Multimodal_Embeddings_CVPR_2026_paper.html) 提出的通用多模态嵌入模型。VisME 基于 [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) 构建，可为图片、文本以及图文对生成稠密嵌入，在通用多模态基准（**MMEB**）和视觉 ID 检索（**MVEB**）上均有出色表现。
 <p align="center">
   <img src="assets/teaser.jpg" alt="VisME teaser" width="95%">
 </p>
-## 亮点
-- **ID 感知检索** —— 通过 [MVEB](https://chrisclear3.github.io/MVEB/) 基准（4 类元任务、28 个测试子集）引入视觉 ID 监督进行训练。
-- **通用能力强** —— 在 **MMEB-v1** 与 **MVEB** 上联合训练，在标准 MMEB 任务上保持有竞争力的表现。
-## 性能
-下图为 **MMEB**（Cls / VQA / Ret / Grd）与 **MVEB**（ID-Rec / Re-ID / ID-Grd / ID-Edit）上的结果。指标与论文一致（各任务组内所有数据集的平均 Precision@1）。
 <p align="center">
   <img src="assets/performance.jpg" alt="VisME performance on MMEB and MVEB" width="95%">
 </p>
-## 训练数据
-VisME 在以下数据的混合集上训练：
-| 基准 | 作用 |
-|------|------|
-| [**MMEB-v1**](https://github.com/TIGER-AI-Lab/VLM2Vec) | 通用多模态嵌入任务（分类、VQA、检索、定位） |
-| [**MVEB**](https://chrisclear3.github.io/MVEB/) | 覆盖识别、Re-ID、定位与编辑的视觉 ID 检索 |
-**MVEB** 数据集已开源，训练代码即将发布。
-## 使用方法
 ```python
 import torch
@@ -79,9 +79,9 @@ batch = [
 ]
 with torch.no_grad():
-    embeddings = model.encode_input(batch)  # 形状: (3, 3584)，已做 L2 归一化
-# 两两余弦相似度
 # SpiderMan_comic_E616  <-> SpiderMan_promo_E199999
 # SpiderMan_comic_E616  <-> Toxin
 # SpiderMan_promo_E199999 <-> Toxin
@@ -91,13 +91,13 @@ for i in range(len(names)):
         print(f"cosine_similarity({names[i]}, {names[j]}) = {sim:.4f}")
 ```
-## 评测
-MMEB 与 MVEB 的基准评测代码**即将发布**。在此之前，完整的实验设置与基线请参见论文。
-## 引用
-如果 VisME 或 MVEB 对您有帮助，请引用：
 ```bibtex
 @inproceedings{cao2026illuminating,
@@ -109,6 +109,6 @@ MMEB 与 MVEB 的基准评测代码**即将发布**。在此之前，完整的
 }
 ```
-## 许可
-本模型基于 [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) 构建。请遵循基础模型以及训练所用各数据集的许可条款。

 # VisME (Qwen2.5-VL-7B)
+English | [简体中文](README-zh.md)
+**VisME** is a universal multimodal embedding model from our CVPR 2026 paper [*Illuminating Visual Identity in Universal Multimodal Embeddings*](https://openaccess.thecvf.com/content/CVPR2026/html/Cao_Illuminating_Visual_Identity_in_Universal_Multimodal_Embeddings_CVPR_2026_paper.html). Built on [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), VisME produces dense embeddings for images, text, and image–text pairs, with strong performance on both general multimodal benchmarks (**MMEB**) and identity-centric retrieval (**MVEB**).
 <p align="center">
   <img src="assets/teaser.jpg" alt="VisME teaser" width="95%">
 </p>
+## Highlights
+- **Identity-aware retrieval** — trained with visual-identity supervision via the [MVEB](https://chrisclear3.github.io/MVEB/) benchmark (4 meta-tasks, 28 test subsets).
+- **Strong general capability** — jointly trained on **MMEB-v1** and **MVEB**, maintaining competitive performance on standard MMEB tasks.
+## Performance
+Results on **MMEB** (Cls / VQA / Ret / Grd) and **MVEB** (ID-Rec / Re-ID / ID-Grd / ID-Edit). Metrics are reported as in the paper (average Precision@1 across datasets within each task group).
 <p align="center">
   <img src="assets/performance.jpg" alt="VisME performance on MMEB and MVEB" width="95%">
 </p>
+## Training Data
+VisME is trained on a mixture of:
+| Benchmark | Role |
+|-----------|------|
+| [**MMEB-v1**](https://github.com/TIGER-AI-Lab/VLM2Vec) | General multimodal embedding tasks (classification, VQA, retrieval, grounding) |
+| [**MVEB**](https://chrisclear3.github.io/MVEB/) | Visual-identity retrieval across recognition, re-ID, grounding, and editing |
+The **MVEB** datasets are now open-sourced. Training code will be released soon.
+## Usage
 ```python
 import torch
 ]
 with torch.no_grad():
+    embeddings = model.encode_input(batch)  # shape: (3, 3584), L2-normalized
+# Pairwise cosine similarity
 # SpiderMan_comic_E616  <-> SpiderMan_promo_E199999
 # SpiderMan_comic_E616  <-> Toxin
 # SpiderMan_promo_E199999 <-> Toxin
         print(f"cosine_similarity({names[i]}, {names[j]}) = {sim:.4f}")
 ```
+## Evaluation
+Benchmark evaluation code for MMEB and MVEB is **coming soon**. In the meantime, please refer to the paper for full experimental settings and baselines.
+## Citation
+If you find VisME or MVEB useful, please cite:
 ```bibtex
 @inproceedings{cao2026illuminating,
 }
 ```
+## License
+This model is built upon [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct). Please follow the license terms of the base model and the respective datasets used during training.