|
|
--- |
|
|
license: apache-2.0 |
|
|
task_categories: |
|
|
- visual-question-answering |
|
|
language: |
|
|
- en |
|
|
pretty_name: BenchLMM |
|
|
size_categories: |
|
|
- n<1K |
|
|
--- |
|
|
|
|
|
# Dataset Card for BenchLMM |
|
|
|
|
|
BenchLMM is a benchmarking dataset focusing on the cross-style visual capability of large multimodal models. It evaluates these models' performance in various visual contexts. |
|
|
|
|
|
## Dataset Details |
|
|
|
|
|
### Dataset Description |
|
|
|
|
|
- **Curated by:** Rizhao Cai, Zirui Song, Dayan Guan, Zhenhao Chen, Xing Luo, Chenyu Yi, and Alex Kot. |
|
|
- **Funded by :** Supported in part by the Rapid-Rich Object Search (ROSE) Lab of Nanyang Technological University and the NTU-PKU Joint Research Institute. |
|
|
- **Shared by :** AIFEG. |
|
|
- **Language(s) (NLP):** English. |
|
|
- **License:** Apache-2.0. |
|
|
|
|
|
### Dataset Sources |
|
|
|
|
|
- **Repository:** [GitHub - AIFEG/BenchLMM](https://github.com/AIFEG/BenchLMM) |
|
|
- **Paper :** Cai, R., Song, Z., Guan, D., et al. (2023). BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models. arXiv:2312.02896. |
|
|
|
|
|
## Uses |
|
|
|
|
|
### Direct Use |
|
|
|
|
|
The dataset can be used to benchmark large multimodal models, especially focusing on their capability to interpret and respond to different visual styles. |
|
|
|
|
|
## Dataset Structure |
|
|
|
|
|
- **Directory Structure:** |
|
|
- `baseline/`: Baseline code for LLaVA and InstructBLIP. |
|
|
- `evaluate/`: Python code for model evaluation. |
|
|
- `evaluate_results/`: Evaluation results of baseline models. |
|
|
- `jsonl/`: JSONL files with questions, image locations, and answers. |
|
|
|
|
|
## Dataset Creation |
|
|
|
|
|
### Curation Rationale |
|
|
|
|
|
Developed to assess large multimodal models' performance in diverse visual contexts, helping to understand their capabilities and limitations. |
|
|
|
|
|
### Source Data |
|
|
|
|
|
#### Data Collection and Processing |
|
|
|
|
|
The dataset consists of various visual questions and corresponding answers, structured to evaluate multimodal model performance. |
|
|
|
|
|
## Bias, Risks, and Limitations |
|
|
|
|
|
Users should consider the specific visual contexts and question types included in the dataset when interpreting model performance. |
|
|
|
|
|
## Citation |
|
|
|
|
|
**BibTeX:** |
|
|
@misc{cai2023benchlmm, |
|
|
title={BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models}, |
|
|
author={Rizhao Cai and Zirui Song and Dayan Guan and Zhenhao Chen and Xing Luo and Chenyu Yi and Alex Kot}, |
|
|
year={2023}, |
|
|
eprint={2312.02896}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CV} |
|
|
} |
|
|
|
|
|
**APA:** |
|
|
Cai, R., Song, Z., Guan, D., Chen, Z., Luo, X., Yi, C., & Kot, A. (2023). BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models. arXiv preprint arXiv:2312.02896. |
|
|
|
|
|
## Acknowledgements |
|
|
|
|
|
This research is supported in part by the Rapid-Rich Object Search (ROSE) Lab of Nanyang Technological University and the NTU-PKU Joint Research Institute. |
|
|
|
