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https://github.com/OpenGVLab/PVC
PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Models
https://github.com/OpenGVLab/PVC
Last synced: 13 days ago
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PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Models
- Host: GitHub
- URL: https://github.com/OpenGVLab/PVC
- Owner: OpenGVLab
- License: mit
- Created: 2024-12-12T07:39:24.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-12-13T03:27:34.000Z (about 2 months ago)
- Last Synced: 2024-12-13T04:20:18.263Z (about 2 months ago)
- Language: Python
- Homepage: https://arxiv.org/abs/2412.09613
- Size: 2.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Progressive Visual Token Compression (PVC)
[](https://arxiv.org/abs/2412.09613)
[](https://huggingface.co/OpenGVLab/PVC-InternVL2-8B)
The official implementation of the paper "[PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Models](https://arxiv.org/abs/2412.09613)".
We introduce the **Progressive Visual Token Compression (PVC)** in large vision-language models (VLMs), which unifies the visual inputs as videos and progressively compresses vision tokens across video frames. Our PVC achieves:
* Preserve spatial details and temporal dynamics for both images and videos.
* Effectively reduce the tokens used for each video frame and image tile.
* SoTA performance on various video benchmarks, including long and fine-grained short video tasks.
* No performance loss on image benchmarks, especially on detail-sensitive tasks.
## 📈 Results
Our implementation is based on the [InternVL2](https://github.com/OpenGVLab/InternVL) model, referred to as **PVCInternVL2**
### Video Understanding Benckmarks
| Model | LLaVA-OneVision-7B | Qwen2-VL-7B | InternVL2-8B | PVCInternVL2-8B
🤗 [link](https://huggingface.co/OpenGVLab/PVC-InternVL2-8B) |
| :--------------: | :--: | :--: | :--: | :--: |
| \# token/frame | 196 | - | 256 | 64 |
| | | | | |
| MVbench | 56.7 | 67.0 | 66.4 | 73.8 |
| VideoMME w/o-sub | 58.2 | 63.3 | 54.0 | 64.1 |
| VideoMME w-sub | 61.5 | 69.0 | 56.9 | 69.7 |
| MLVU | 64.7 | - | 52.0 | 72.4 |
| LongVideoBench | 56.5 | - | - | 59.2 |
| NextQA | 79.4 | - | - | 82.0 |
| Egoschema | 60.1 | 66.7 | 55.0 | 59.6 |
| PercepTest | 57.1 | 62.3 | 52.0 | 68.4 |
| AcNet-QA | 56.6 | - | - | 57.1 |
### Image Understanding Benckmarks
| Model | LLaVA-OneVision-7B | Qwen2-VL-7B | InternVL2-8B | PVCInternVL2-8B
🤗 [link](https://huggingface.co/OpenGVLab/PVC-InternVL2-8B) |
| :--------------------: | :--: | :--: | :--: | :--: |
| \# token/image tile | 729 | - | 256 | 64 |
| | | | | |
| AI2Dtest | 81.4 | 83.0 | 83.8 | 83.8 |
| ChartQAtest | 80.0 | 83.0 | 83.3 | 84.1 |
| DocVQAtest | 87.5 | 94.5 | 91.6 | 92.5 |
| InfoVQAtest | 68.8 | 76.5 | 74.8 | 75.0 |
| SQAtest | 96.0 | - | 97.1 | 97.7 |
| TextVQAval | - | 84.3 | 77.4 | 80.0 |
| MMBen-test | - | 83.0 | 81.7 | 83.9 |
| MMEsum | 1998 | 2327 | 2210 | 2282 |
| MMMUval | 48.8 | 54.1 | 49.3 | 50.9 |
| SEEDI | 75.4 | - | 76.2 | 77.2 |
| OCRBench | - | 866 | 794 | 807 |
## 🛠️ Usage
You can use `pip install -r requirements.txt` to set up the environment. Please use `transformers>=4.37.2` to ensure the model works normally.
```python
import torch
from transformers import AutoTokenizer, AutoModel
from utils.preprocess import load_image, load_video
path = 'OpenGVLab/PVC-InternVL2-8B'
model = AutoModel.from_pretrained(
path,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
generation_config = dict(max_new_tokens=1024, do_sample=True)
# single-image conversation
pixel_values = load_image('./assets/example_image1.jpg', max_num=12).to(torch.bfloat16).cuda()
data_flag = torch.tensor([1], dtype=torch.long).cuda()
question = '\nWhat is in the image?'
response = model.chat(tokenizer, pixel_values, question, generation_config, data_flag=data_flag)
print(f'User: {question}\nAssistant: {response}')
# multi-image conversation
pixel_values1 = load_image('./assets/example_image1.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values2 = load_image('./assets/example_image2.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
data_flag = torch.tensor([2], dtype=torch.long).cuda()
num_patches_list = [pixel_values1.shape[0], pixel_values2.shape[0]]
question = 'Image-1: \nImage-2: \nWhat are the similarities and differences between these two images.'
response = model.chat(tokenizer, pixel_values, question, generation_config, data_flag=data_flag, num_patches_list=num_patches_list)
print(f'User: {question}\nAssistant: {response}')
# video conversation
pixel_values, num_patches_list = load_video('./assets/example_video.mp4', num_segments=64, max_num=1)
pixel_values = pixel_values.to(torch.bfloat16).cuda()
video_prefix = ''.join([f'Frame{i+1}: \n' for i in range(len(num_patches_list))])
# Frame1: \nFrame2: \n...\nFrameN: \n{question}
data_flag = torch.tensor([3], dtype=torch.long).cuda()
question = video_prefix + 'Describe this video in detail.'
response = model.chat(tokenizer, pixel_values, question, generation_config, data_flag=data_flag, num_patches_list=num_patches_list)
print(f'User: {question}\nAssistant: {response}')
```
## 📊 Evaluation
### Image Benchmarks & MVBench
**Prepare data:** please follow [here](https://internvl.readthedocs.io/en/latest/get_started/eval_data_preparation.html) to prepare the data for evaluation.
**Run evaluation:** use the following command to start the evaluation:
```bash
bash evaluate_launch.sh
```
Currently supported tasks: `vqa-ai2d-test`, `vqa-chartqa-test`, `vqa-docvqa-val`, `vqa-docvqa-test`, `vqa-infovqa-val`, `vqa-infovqa-test`, `scienceqa`, `mme`, `mmbench-dev-en`, `mmbench-test-en`, `mmmu-val`, `seed`, `mvbench`.
For image benchmarks and MVBench, we use the evaluation codebase of InternVL2. Refer to [here](https://internvl.readthedocs.io/en/latest/internvl2.0/evaluation.html#) for more details.
## 📅 TODO List
* [X] release model and checkpoint
* [ ] release evaluation code
* [ ] release training code
## 🖊️ Citation
If you find this work helpful in your research, please consider citing:
```bibtex
@article{yang2024pvc,
title={PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Models},
author={Yang, Chenyu and Dong, Xuan and Zhu, Xizhou and Su, Weijie and Wang, Jiahao and Tian, Hao and Chen, Zhe and Wang, Wenhai and Lu, Lewei and and Dai, Jifeng},
journal={arXiv preprint arXiv:2412.09613},
year={2024}
}
```
## 📃 License
This project is released under the [MIT license](LICENSE). Parts of this project contain code and models from other sources, which are subject to their respective licenses.