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Add MMGenBench #200

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Expand Up @@ -430,6 +430,7 @@ This is the first work to correct hallucination in multimodal large language mod
## Evaluation
| Title | Venue | Date | Page |
|:--------|:--------:|:--------:|:--------:|
| ![Stars](https://img.shields.io/github/stars/lerogo/MMGenBench?style=social&label=Star) <br> [**MMGenBench: Evaluating the Limits of LMMs from the Text-to-Image Generation Perspective**](https://arxiv.org/abs/2411.14062) <br> | arXiv | 2024-11-21 | [Github](https://github.com/lerogo/MMGenBench) |
| ![Stars](https://img.shields.io/github/stars/multimodal-art-projection/OmniBench?style=social&label=Star) <br> [**OmniBench: Towards The Future of Universal Omni-Language Models**](https://arxiv.org/pdf/2409.15272) <br> | arXiv | 2024-09-23 | [Github](https://github.com/multimodal-art-projection/OmniBench) |
| ![Stars](https://img.shields.io/github/stars/yfzhang114/MME-RealWorld?style=social&label=Star) <br> [**MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?**](https://arxiv.org/pdf/2408.13257) <br> | arXiv | 2024-08-23 | [Github](https://github.com/yfzhang114/MME-RealWorld) |
| ![Stars](https://img.shields.io/github/stars/guoyang9/UNK-VQA?style=social&label=Star) <br> [**UNK-VQA: A Dataset and a Probe into the Abstention Ability of Multi-modal Large Models**](https://arxiv.org/pdf/2310.10942) <br> | TPAMI | 2023-10-17 | [Github](https://github.com/guoyang9/UNK-VQA) |
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## Benchmarks for Evaluation
| Name | Paper | Link | Notes |
|:-----|:-----:|:----:|:-----:|
| **MMGenBench** | [MMGenBench: Evaluating the Limits of LMMs from the Text-to-Image Generation Perspective](https://arxiv.org/abs/2411.14062) | [Link](https://github.com/lerogo/MMGenBench) | A benchmark that only uses images and text-to-image models for evaluation |
| **LiveXiv** | [LiveXiv -- A Multi-Modal Live Benchmark Based on Arxiv Papers Content](https://arxiv.org/pdf/2410.10783) | [Link](https://huggingface.co/datasets/LiveXiv/LiveXiv) | A live benchmark based on arXiv papers |
| **TemporalBench** | [TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models](https://arxiv.org/pdf/2410.10818) | [Link](https://huggingface.co/datasets/microsoft/TemporalBench) | A benchmark for evaluation of fine-grained temporal understanding |
| **OmniBench** | [OmniBench: Towards The Future of Universal Omni-Language Models](https://arxiv.org/pdf/2409.15272) | [Link](https://huggingface.co/datasets/m-a-p/OmniBench) | A benchmark that evaluates models' capabilities of processing visual, acoustic, and textual inputs simultaneously |
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