Progressive Visual Token Compression (PVC)
June 12, 2025 · View on GitHub
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 model, referred to as PVCInternVL2
Video Understanding Benckmarks
| Model | LLaVA-OneVision-7B | Qwen2-VL-7B | InternVL2-8B | PVCInternVL2-8B 🤗 link |
|---|---|---|---|---|
| # 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 |
|---|---|---|---|---|
| # 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.
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 = '<image>\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: <image>\nImage-2: <image>\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}: <image>\n' for i in range(len(num_patches_list))])
# Frame1: <image>\nFrame2: <image>\n...\nFrameN: <image>\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 to prepare the data for evaluation.
Run evaluation: use the following command to start the evaluation:
bash evaluate_launch.sh <checkpoint> <task>
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 for more details.
🖊️ Citation
If you find this work helpful in your research, please consider citing:
@inproceedings{yang2025pvc,
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 Dai, Jifeng},
booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
pages={24939--24949},
year={2025}
}
📃 License
This project is released under the MIT license. Parts of this project contain code and models from other sources, which are subject to their respective licenses.