Awesome

DSM

The source code for paper Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion

Datasets list and some visualizations/provided weights are preparing now.

1. Introduction (scene-dominated to motion-dominated)

Video datasets are usually scene-dominated, We propose to decouple the scene and the motion (DSM) with two simple operations, so that the model attention towards the motion information is better paid.

The generated triplet is as below:

What DSM learned?

With DSM pretrain, the model learn to focus on motion region (Not necessarily actor) powerful without one label available.

2. Installation

Dataset

Please refer dataset.md for details.

Requirements

Python3
pytorch1.1+
PIL
Intel (on the fly decode)

3. Structure

datasets
- list
  - hmdb51: the train/val lists of HMDB51
  - ucf101: the train/val lists of UCF101
  - kinetics-400: the train/val lists of kinetics-400
  - diving48: the train/val lists of diving48
experiments
- logs: experiments record in detials
- gradientes: grad check
- visualization:
src
- data: load data
- loss: the loss evaluate in this paper
- model: network architectures
- scripts: train/eval scripts
- augment: detail implementation of Spatio-temporal Augmentation
- utils
- feature_extract.py: feature extractor given pretrained model
- main.py: the main function of finetune
- trainer.py
- option.py
- pt.py: self-supervised pretrain
- ft.py: supervised finetune

DSM(Triplet)/DSM/Random

Self-supervised Pretrain

Kinetics

bash scripts/kinetics/pt.sh

UCF101

bash scripts/ucf101/pt.sh

Supervised Finetune (Clip-level)

HMDB51

bash scripts/hmdb51/ft.sh

UCF101

bash scripts/ucf101/ft.sh

Kinetics

bash scripts/kinetics/ft.sh

Video-level Evaluation

Following common practice TSN and Non-local. The final video-level result is average by 10 temporal window sampling + corner crop, which lead to better result than clip-level. Refer test.py for details.

Pretrain And Eval In one step

bash scripts/hmdb51/pt_and_ft_hmdb51.sh

Notice: More Training Options and ablation study Can be find in scripts

Video Retrieve and other visualization

(1). Feature Extractor

As STCR can be easily extend to other video representation task, we offer the scripts to perform feature extract.

python feature_extractor.py

The feature will be saved as a single numpy file in the format [video_nums,features_dim] for further visualization.

(2). Reterival Evaluation

modify line60-line62 in reterival.py.

python reterival.py

Results

Action Recognition

UCF101 Pretrained (I3D)

Method	UCF101	HMDB51
Random Initialization	47.9	29.6
MoCo Baseline	62.3	36.5
DSM(Triplet)	70.7	48.5
DSM	74.8	52.5

Kinetics Pretrained

Video Retrieve (UCF101-C3D)

Method	@1	@5	@10	@20	@50
DSM	16.8	33.4	43.4	54.6	70.7

Video Retrieve (HMDB51-C3D)

Method	@1	@5	@10	@20	@50
DSM	8.2	25.9	38.1	52.0	75.0

More Visualization

Acknowledgement

This work is partly based on STN, UEL and MoCo.

License

Citation

If you use our code in your research or wish to refer to the baseline results, pleasuse use the followint BibTex entry.

@inproceedings{wang2021enhancing,
  title={Enhancing unsupervised video representation learning by decoupling the scene and the motion},
  author={Wang, Jinpeng and Gao, Yuting and Li, Ke and Hu, Jianguo and Jiang, Xinyang and Guo, Xiaowei and Ji, Rongrong and Sun, Xing},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={11},
  pages={10129--10137},
  year={2021}
}