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https://github.com/OpenGVLab/MMIU

MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models
https://github.com/OpenGVLab/MMIU

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MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models

Host: GitHub
URL: https://github.com/OpenGVLab/MMIU
Owner: OpenGVLab
Created: 2024-08-05T15:28:20.000Z (4 months ago)
Default Branch: main
Last Pushed: 2024-08-14T02:28:02.000Z (4 months ago)
Last Synced: 2024-08-14T08:32:40.201Z (4 months ago)
Language: Python
Homepage: https://mmiu-bench.github.io/
Size: 1.3 MB
Stars: 24
Watchers: 0
Forks: 0
Open Issues: 1
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

Awesome-LLMs-Datasets - https://github.com/OpenGVLab/MMIU

README

        # Best Practice

**We strongly recommend using [VLMEevalKit](https://github.com/open-compass/VLMEvalKit) for its useful features and ready-to-use LVLM implementations**.

# MMIU



  Quick Start |

  HomePage |

  arXiv |

  Dataset |

  Citation 




This repository is the official implementation of [MMIU](https://arxiv.org/abs/2408.02718). 

> [MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models](https://arxiv.org/abs/2408.02718)  

> Fanqing Meng^\*, Jin Wang^\*, Chuanhao Li^\*, Quanfeng Lu, Hao Tian, Jiaqi Liao, Xizhou Zhu, Jifeng Dai,  Yu Qiao, Ping Luo, Kaipeng Zhang^\#, Wenqi Shao^\#  

> ^\* MFQ, WJ and LCH contribute equally.  

> ^\# SWQ ([email protected]) and ZKP ([email protected]) are correponding authors. 

## 💡 News

- `2024/08/13`: We have released the codes. 

- `2024/08/08`: We have released the dataset at https://huggingface.co/datasets/FanqingM/MMIU-Benchmark 🔥🔥🔥

- `2024/08/05`: The datasets and codes are coming soon! 🔥🔥🔥

- `2024/08/05`: The technical report of [MMIU](https://arxiv.org/abs/2408.02718) is released! And check our [project page](https://mmiu-bench.github.io/)! 🔥🔥🔥

## Introduction

Multimodal Multi-image Understanding (MMIU) benchmark, a comprehensive evaluation suite designed to assess LVLMs across a wide range of multi-image tasks. MMIU encompasses 7 types of multi-image relationships, 52 tasks, 77K images, and 11K meticulously curated multiple-choice questions, making it the most extensive benchmark of its kind. 

![overview](assets/overview.jpg)

## Evaluation Results Overview

- The closed-source proprietary model GPT-4o from OpenAI has taken a leading position in MMIU, surpassing other models such as InternVL2-pro, InternVL1.5-chat, Claude3.5-Sonnet, and Gemini1.5 flash. Note that the open-source models InternVL2-pro.

- Some powerful LVLMs like InternVL1.5  and  GLM4V whose pre-training data do not contain multi-image content even outperform many multi-image models which undergo multi-image supervised fine-tuning (SFT), indicating the strong capacity in single-image understanding is the foundation of multi-image comprehension.

- By comparing performance at the level of image relationships, we conclude that LVLM excels at understanding semantic content in multi-image scenarios but has weaker performance in comprehending temporal and spatial relationships in multi-image contexts.

- The analysis based on the task map reveals that models perform better on high-level understanding tasks such as video captioning which are in-domain tasks, but struggle with 3D perception tasks such as 3D detection and temporal reasoning tasks such as image ordering which are out-of-domain tasks.

- By task learning difficulty analysis, tasks involving ordering, retrieval and massive images cannot be overfitted by simple SFT, suggesting that additional pre-training data or training techniques should be incorporated for improvement.

![taskmap](assets/taskmap.jpg)

## 🏆 Leaderboard

| Rank | Model | Score |

| ---- | ---------------------- | ----- |

| **1** | **GPT4o** | **55.72** |

| 2 | Gemini | 53.41 |

| 3 | Claude3 | 53.38 |

| **4** | **InternVL2** | **50.30** |

| 5 | Mantis | 45.58 |

| 6 | Gemini1.0 | 40.25 |

| 7 | internvl1.5-chat | 37.39 |

| 8 | Llava-interleave | 32.37 |

| 9 | idefics2_8b | 27.80 |

| 10 | glm-4v-9b | 27.02 |

| 11 | deepseek_vl_7b | 24.64 |

| 12 | XComposer2_1.8b | 23.46 |

| 13 | deepseek_vl_1.3b | 23.21 |

| 14 | flamingov2 | 22.26 |

| 15 | llava_next_vicuna_7b | 22.25 |

| 16 | XComposer2 | 21.91 |

| 17 | MiniCPM-Llama3-V-2_5 | 21.61 |

| 18 | llava_v1.5_7b | 19.19 |

| 19 | sharegpt4v_7b | 18.52 |

| 20 | sharecaptioner | 16.10 |

| 21 | qwen_chat | 15.92 |

| 22 | monkey-chat | 13.74 |

| 23 | idefics_9b_instruct | 12.84 |

| 24 | qwen_base | 5.16 |

| -   | Frequency Guess        | 31.5  |

| -   | Random Guess           | 27.4  |

## 🚀 Quick Start

Here, we mainly use the VLMEvalKit framework for testing, with some separate tests as well. Specifically, for multi-image models, we include the following models:

**transformers == 33.0**

- `XComposer2`

- `XComposer2_1.8b`

- `qwen_base`

- `idefics_9b_instruct`

- `qwen_chat`

- `flamingov2`

**transformers == 37.0**

- `deepseek_vl_1.3b`

- `deepseek_vl_7b`

**transformers == 40.0**

- `idefics2_8b`

For single-image models, we include the following:

**transformers == 33.0**

- `sharecaptioner`

- `monkey-chat`

**transformers == 37.0**

- `sharegpt4v_7b`

- `llava_v1.5_7b`

- `glm-4v-9b`

**transformers == 40.0**

- `llava_next_vicuna_7b`

- `MiniCPM-Llama3-V-2_5`

We use the VLMEvalKit framework for testing. You can refer to the code in `VLMEvalKit/test_models.py`. Additionally, for closed-source models, please replace the following part of the code by following the example here:

```python

response = model.generate(tmp) # tmp = image_paths + [question]

```

For other open-source models, we have provided reference code for `Mantis` and `InternVL1.5-chat`. For `LLava-Interleave`, please refer to the original repository.

## 💐 Acknowledgement

We expressed sincerely gratitude for the projects listed following:

- [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) provides useful out-of-box tools and implements many adavanced LVLMs. Thanks for their selfless dedication.

- The Team of InternVL for apis.

## 📧 Contact

If you have any questions, feel free to contact Fanqing Meng with [email protected]

## 🖊️ Citation 

If you feel MMIU useful in your project or research, please kindly use the following BibTeX entry to cite our paper. Thanks!

```

@article{meng2024mmiu,

  title={MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models},

  author={Meng, Fanqing and Wang, Jin and Li, Chuanhao and Lu, Quanfeng and Tian, Hao and Liao, Jiaqi and Zhu, Xizhou and Dai, Jifeng and Qiao, Yu and Luo, Ping and others},

  journal={arXiv preprint arXiv:2408.02718},

  year={2024}

}

```