Yingjie Zhai, Wenshuo Li, Yehui Tang, Xinghao Chen, Yunhe Wang
arXiv 2024
This is an official pytorch implementation of our paper "No Time to Waste: Squeeze Time into Channel for Mobile Video Understanding". In this paper, we propose to squeeze the time axis of a video sequence into the channel dimension and present a lightweight video recognition network, term as SqueezeTime, for mobile video understanding. To enhance the temporal modeling capability of the model, we design a Channel-Time Learning (CTL) Block to capture temporal dynamics of the sequence. This module has two complementary branches, in which one branch is for temporal importance learning and another branch with temporal position restoring capability is to enhance inter-temporal object modeling ability. The proposed SqueezeTime is much lightweight and fast with high accuracies for mobile video understanding. Extensive experiments on various benchmarks, i.e., Kinetics400, Kinetics600, SomethingSomethingV2, HMDB51, AVA2.1, and THUMOS14, demonstrate the superiority of our model. For example, our SqueezeTime achieves +1.2% accuracy and +80% GPU throughput gain on Kinetics400 than prior methods.
Figure 1: Pipeline of the SqueezeTime.
The models is based on the mmaction2 project. Please run the models by mmaction2.
- Clone the mmaction2 project
git clone https://github.com/open-mmlab/mmaction2.gitThen install mmaction2 following the offical instruction.
- Clone this repository
git clone https://github.com/xinghaochen/SqueezeTime.git
cd SqueezeTime- Copy our model files
mmaction/models/backbones/SqueezeTime.py
mmaction/models/backbones/SqueezeTime_ava.py
mmaction/models/heads/i2d_head.py
into the mmaction folder following the original structure. Then add the reference in the "_init_.py" of backbones and heads files of the mmaction.
- Copy the "ckpt", "configs", and "data" folders into the mmaction2 project following the original sturcture.
All dataset are organized using mmaction2 format. Please organize the data directory as follows after downloading all of them:
-
Data Structure Tree
├── data ├── kinetics400 │ ├── kinetics400_train_list_videos.txt │ ├── kinetics400_val_list_videos.txt │ └── kinetics_videos/ ├── kinetics600 │ ├── kinetics400_train_list_videos.txt │ ├── kinetics400_val_list_videos.txt │ └── videos/ ├── hmdb51 │ ├── hmdb51_train_split_1_videos.txt │ ├── hmdb51_train_split_2_videos.txt │ ├── hmdb51_train_split_3_videos.txt │ ├── hmdb51_val_split_1_videos.txt │ ├── hmdb51_val_split_2_videos.txt │ ├── hmdb51_val_split_3_videos.txt │ └── videos/ └── ava ├── ava_train_v2.1.csv ├── ava_val_v2.1.csv └── rawframes/
All config files are in 'configs/recognition/' and 'configs/detection/'. The configs for train and test K400, K600, HMDB51, sthv2, and AVA2.1 datasets are in
"configs/recognition/SqueezeTime/SqueezeTime_K400.py",
"configs/recognition/SqueezeTime/SqueezeTime_K600.py",
"configs/recognition/SqueezeTime/SqueezeTime_HMDB51.py",
"configs/detection/SqueezeTime_AVA2.1.py".
Take the Kinectics400 dataset for an example:
-
Train the SqueezeTime on K400:
bash tools/dist_train.sh configs/recognition/SqueezeTime/SqueezeTime_K400.py 8 -
Test the SqueezeTime on K400:
bash tools/dist_test.sh configs/recognition/SqueezeTime/SqueezeTime_K400.py ckpts/SqueezeTime_K400_71.64.pth 8
Besides, we also provide the configs of other models trained in our paper, please find them in the "configs" folder.
| Dataset | Checkpoints |
|---|---|
| ImageNet1K | Download |
| K400 | Download |
| K600 | Download |
| HMDB51 | Download |
| AVA2.1 | Download |
The following results are on the Kinetics400 dataset. Please see the paper for the results on other datasets.
Figure2. Performace comparison of multiple lightweight methods on K400 dataset.
Figure3. Speed comparison of methods on K400 dataset. (a) CPU and GPU speed, (b) Mobile Phone CPU Latency.
If you find SqueezeTime useful in your research or applications, please consider giving a star ⭐ and citing using the following BibTeX:
@article{zhai2024SqueezeTime,
title={No Time to Waste: Squeeze Time into Channel for Mobile Video Understanding},
author={Zhai, Yingjie and Li, Wenshuo and Tang, Yehui and Chen, Xinghao and Wang, Yunhe},
journal={arXiv preprint arXiv:2405.08344},
year={2024}
}