Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs

Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, Saining Xie

Paper | Project Page | MMVP Benchmark

Contents:

1. Getting Started
2. Benchmark
3. Evaluation
4. Training
5. License
6. Citation
7. Acknowledgement

Getting Started

Installation

conda create -n mmvp python=3.10 -y
conda activate mmvp
cd LLaVA
pip install -e .
pip install flash-attn --no-build-isolation

Pre-trained Model

The Interleaved-MoF Models (Based on LLaVA-1.5 13b) can be found here.

Benchmarks

MMVP Bechmark

Our MMVP Benchmark is available here. It is specially crafted to measure multimodal LLM's visual capability via VQA. The benchmark is seperated into a folder containing all 300 testing images and an annotation csv file with questions and correct answers. The format of the data is:

├── MMVP Images
│   ├── 1.jpg
│   ├── 2.jpg
│   ├── 3.jpg
│   ├── ...
│   └── 300.jpg
└── Questions.csv

MMVP VLM Bechmark

Our MMVP-VLM Benchmark is available here. It is distilled from the MMVP benchmark above and simplified the language description for each image. It is designed to evaluate VLMs such as CLIP's visual capability. The benchmark is organized into 9 different visual patterns. In each visual pattern, there are 15 pairs of zero-shot questions. An annotation csv file contains the question, correponding visual pattern and images. The format of the data is:

├── MMVP_VLM_Images
│   ├── Orientation
│   │   ├── 1.jpg
│   │   ├── 2.jpg
│   │   ├── ...
│   │   └── 30.jpg
│   ├── Presence
│   │   ├── 31.jpg
│   │   ├── 32.jpg
│   │   ├── ...
│   │   └── 60.jpg
│   ├── ...
│   └── Camera_Perspective
│       ├── 241.jpg
│       ├── 242.jpg
│       ├── ...
│       └── 270.jpg
└── Questions.csv

Evaluation

To evaluate on the MMVP, run

python scripts/evaluate_mllm.py --directory PATH_TO_MMVP_BENCHMARK_FOLDER --model-path PATH_TO_MODEL_EVALUATED

The script provide an evaluation for LLaVA based models that generate a jsonl file containing questions, correct answer and model response. Feel free to modify the script and apply on other models.

After generating model's response, one can manually check the accuracy or use a LLM (e.g. GPT-4) to generate the score.

python scripts/gpt_grader.py --openai_api_key YOUR_OPENAI_API_KEY --answer_file PATH_TO_MODEL_RESPONSE_EVALUATED

Here is the result of SOTA models on MMVP Benchmark. It shows that these leading models consistently struggles with these straightforward questions on visual grouding,

To evaluate on MMVP-VLM, run

python scripts/evaluate_vlm.py --directory PATH_TO_MMVPVLM_BENCHMARK_FOLDER

Here is the result of SOTA CLIP models on MMVP-VLM Benchmark. It shows that scaling up parameters and image resolution in CLIP models obtain very little improvement on discerning these visual patterns.

Training

The training of the Interleaved-MoF MLLM model follows the training procedure of LLaVA. Please follow the data preparation process in LLaVA. Please replace the directory to data in the training data to your local directories.

For the pre-training stage, enter the LLaVA folder and run

sh pretrain.sh

For the instruction tuning stage, enter the LLaVA folder and run

sh finetune.sh

One can also find the plug and play changes nessecary to Interleaved-MoF in "LLaVA/llava/model/llava_arch.py/#L155". The function prepare_inputs_labels_for_multimodal_withdino contains ways to spatially interleave DINOv2 and CLIP features before feeding to the LLM.

License

This project is under the MIT license. See LICENSE for details.

Citation

Please consider citing our paper if you find this project helpful for your research:

@misc{tong2024eyes,
      title={Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs}, 
      author={Shengbang Tong and Zhuang Liu and Yuexiang Zhai and Yi Ma and Yann LeCun and Saining Xie},
      year={2024},
      eprint={2401.06209},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Acknowledgement

• This work is built upon the LLaVA .