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language:
- en
license: apache-2.0
size_categories:
- 100B<n<1T
task_categories:
- multiple-choice
- question-answering
- visual-question-answering
- image-text-to-text
---
* **`2024.08.20`** 🌟 We are proud to open-source MME-Unify, a comprehensive evaluation framework designed to systematically assess U-MLLMs. Our Benchmark covers 10 tasks with 30 subtasks, ensuring consistent and fair comparisons across studies.
Paper: https://arxiv.org/abs/2504.03641
Code: https://github.com/MME-Benchmarks/MME-Unify
Project page: https://mme-unify.github.io/
## How to use?
You can download images in this repository and the final structure should look like this:
```
MME-Unify
├── CommonSense_Questions
├── Conditional_Image_to_Video_Generation
├── Fine-Grained_Image_Reconstruction
├── Math_Reasoning
├── Multiple_Images_and_Text_Interlaced
├── Single_Image_Perception_and_Understanding
├── Spot_Diff
├── Text-Image_Editing
├── Text-Image_Generation
├── Text-to-Video_Generation
├── Video_Perception_and_Understanding
└── Visual_CoT
```
## Dataset details
We present MME-Unify, a comprehensive evaluation framework designed to assess U-MLLMs systematically. Our benchmark includes:
1. **Standardized Traditional Task Evaluation** We sample from 12 datasets, covering 10 tasks with 30 subtasks, ensuring consistent and fair comparisons across studies.
2. **Unified Task Assessment** We introduce five novel tasks testing multimodal reasoning, including image editing, commonsense QA with image generation, and geometric reasoning.
3. **Comprehensive Model Benchmarking** We evaluate 12 leading U-MLLMs, such as Janus-Pro, EMU3, and VILA-U, alongside specialized understanding (e.g., Claude-3.5) and generation models (e.g., DALL-E-3).
Our findings reveal substantial performance gaps in existing U-MLLMs, highlighting the need for more robust models capable of handling mixed-modality tasks effectively.
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