ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to Video（ECCV2024)

This repo is the official implementation of "ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to Video"（ECCV2024)

If you're interested in our work, check out our new video adaptation benchmark!

TODO

Release source codes
Pretrained model weights

Introduction

In this paper, we present a zero-cost adaptation paradigm (ZeroI2V) to transfer the image transformers to video recognition tasks (i.e., introduce zero extra cost to the adapted models during inference).

Models

You could reparameter the weight refer to tools/weight_reparam.py.

Kinetics 400

Backbone	Pretrain	GFLOPs	Param	acc@1	Views	Config	Checkpoint (before reparam)
ViT-B/16	CLIP	422	86	83.0	8x1x3	config	checkpoint
ViT-L/14	CLIP	1946	304	86.3	8x1x3	config	checkpoint
ViT-L/14	CLIP	7783	304	87.2	32x1x3	config	checkpoint

Something Something V2

Backbone	Pretrain	GFLOPs	Param	New Param (M)	acc@1	Views	Config	Checkpoint (before reparam)
ViT-L/14	CLIP	7783	304	0	72.2	32x3x1	config	checkpoint

Installation

pip install -U openmim
mim install mmengine 'mmcv>=2.0.0rc1'
mim install "mmdet>=3.0.0rc5"
mim install "mmpose>=1.0.0rc0"
git clone https://github.com/leexinhao/ZeroI2V.git
cd ZeroI2V
pip install -v -e .
# install CLIP
pip install git+https://github.com/openai/CLIP.git

Our project is based on MMAction2. Please refer to install.md for more detailed instructions.

Data Preparation

All the datasets (K400, SSv2, UCF101 and HMDB51) used in this work are supported in MMAction2.

Training

The training configs of different experiments are provided in configs/recognition/. To run experiments, please use the following command. PATH/TO/CONFIG is the training config you want to use. The default training setting is 8GPU with a batchsize of 64.

bash tools/dist_train.sh <PATH/TO/CONFIG> <NUM_GPU>

We also provide a training script in run_exp.sh. You can simply change the training config to train different models.

Evaluation

The code will do the evaluation after training. If you would like to evaluate a model only, please use the following command,

bash tools/dist_test.sh <PATH/TO/CONFIG> <CHECKPOINT_FILE> <NUM_GPU> --eval top_k_accuracy

Reparameterize the linear adapter

Please refer to tools/weight_reparam.py.

Test speed and throughput

Please refer to tools/test_speed.py and tools/test_throughput.py.

If you find our work useful in your research, please cite:

@article{li2023zeroi2v,
  title={ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to Video},
  author={Li, Xinhao and Zhu, Yuhan and Wang, Limin},
  journal={arXiv preprint arXiv:2310.01324},
  year={2023}
}