Progressive Visual Token Compression (PVC)

The official implementation of the paper "PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Models".

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 PVC_InternVL2

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.

📅 TODO List

• release model and checkpoint
• release evaluation code
• release training code

🖊️ Citation

If you find this work helpful in your research, please consider citing:

@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. Parts of this project contain code and models from other sources, which are subject to their respective licenses.