Official code for Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework [ACMMM'20]
This is my experimental enviroment.
PyTorch 1.3.0 python 3.7.4
For samples, we have
- Inter-positives: samples with same labels, not used for self-supervised learning;
- Inter-negatives: different samples, or samples with different indexes;
- Intra-positives: data from the same sample, in different views / from different augmentations;
- Intra-negatives: data from the same sample while some kind of information has been broken down. In video case, temporal information has been destoried.
Our work makes use of all usable parts (in this classification category) to form an inter-intra contrastive framework. The experiments here are mainly based on Contrastive Multiview Coding. It is flexible to extend this framework to other contrastive learning methods such as MoCo and SimCLR.
Except for inter-negative samples, all possible data are used to help train the network. This inter-intra learning framework can make the most use of data in contrastive learning.
The inter-intra learning framework can be extended to
- Different contrastive learning methods: CMC, MoCo, SimCLR ...
- Different intra-negative generation methods: frame repeating, frame shuffling ...
- Different backbone: C3D, R3D, R(2+1)D, I3D ...
You can download UCF101/HMDB51 dataset from official website: UCF101 and HMDB51. Then decoded videos to frames.
I highly recommend the pre-comupeted optical flow images and resized RGB frames in this repo.
python train_ssl.py --dataset=ucf101
The default setting uses frame repeating as intra-negative samples for videos. R3D is used by default. You can use --model to try different models.
We use two views in our experiments. View #1 is a RGB video clip, View #2 can be RGB/Res/Optical flow video vlip. Residual video clips are default modality for View # 2. You can use --modality to try other modalities. Intra-negative samples are generated from View #1.
In this part, it may be wired to use only one optical flow channel u or v. We use only one channel to make it possible for only one model to handle inputs from different modalities. It is also an optional setting that using different models to handle each modality.
python retrieve_clips.py --ckpt=/path/to/your/model --dataset=ucf101 --merge=True
Only one model is used for different views. You can set --modality to decide which modality to use. When setting --merge=True, RGB for View #1 and the specific modality for View #2 will be jointly tested.
python ft_classify.py --ckpt=/path/to/your/model --dataset=ucf101
The table lists retrieval results on UCF101 split 1. We reimplemented CMC and report its results. Other results are from corresponding paper.
| Method | top1 | top5 | top10 | top20 | top50 |
|---|---|---|---|---|---|
| Jigsaw | 19.7 | 28.5 | 33.5 | 40.0 | 49.4 |
| OPN | 19.9 | 28.7 | 34.0 | 40.6 | 51.6 |
| R3D (random) | 9.9 | 18.9 | 26.0 | 35.5 | 51.9 |
| VCOP | 14.1 | 30.3 | 40.4 | 51.1 | 66.5 |
| VCP | 18.6 | 33.6 | 42.5 | 53.5 | 68.1 |
| CMC | 26.4 | 37.7 | 45.1 | 53.2 | 66.3 |
| Ours (repeat + res) | 36.5 | 54.1 | 62.9 | 72.4 | 83.4 |
| Ours (repeat + u) | 41.8 | 60.4 | 69.5 | 78.4 | 87.7 |
| Ours (shuffle + res) | 34.6 | 53.0 | 62.3 | 71.7 | 82.4 |
| Ours (shuffle + v) | 42.4 | 60.9 | 69.2 | 77.1 | 86.5 |
| RTT | 26.1 | 48.5 | 59.1 | 69.6 | 82.8 |
| MemDPC | 20.2 | 40.4 | 52.4 | 64.7 | - |
We only use R3D as our network backbone. Usually, using Resnet-18-3D, R(2+1)D or deeper networks can yield better performance.
| Method | UCF101 | HMDB51 |
|---|---|---|
| Jigsaw | 51.5 | 22.5 |
| O3N (res) | 60.3 | 32.5 |
| OPN | 56.3 | 22.1 |
| OPN (res) | 71.8 | 36.7 |
| CrossLearn | 58.7 | 27.2 |
| CMC (3 views) | 59.1 | 26.7 |
| R3D (random) | 54.5 | 23.4 |
| ImageNet-inflated | 60.3 | 30.7 |
| 3D ST-puzzle | 65.8 | 33.7 |
| VCOP | 64.9 | 29.5 |
| VCP | 66.0 | 31.5 |
| Ours (repeat + res) | 72.8 | 35.3 |
| Ours (repeat + u) | 72.7 | 36.8 |
| Ours (shuffle + res) | 74.4 | 38.3 |
| Ours (shuffle + v) | 67.0 | 34.0 |
Residual clips + 3D CNN The residual clips with 3D CNNs are effective. More information about this part can be found in Rethinking Motion Representation: Residual Frames with 3D ConvNets for Better Action Recognition (previous but more detailed version) and Motion Representation Using Residual Frames with 3D CNN (short version with better results).
The key code for this part is
shift_x = torch.roll(x,1,2)
x = ((shift_x -x) + 1)/2
Which is slightly different from that in papers.
If you find our work helpful for your research, please consider citing the paper
@article{tao2020rethinking,
title={Rethinking Motion Representation: Residual Frames with 3D ConvNets for Better Action Recognition},
author={Tao, Li and Wang, Xueting and Yamasaki, Toshihiko},
journal={arXiv preprint arXiv:2001.05661},
year={2020}
}
If you find the residual input helpful for video-related tasks, please consider citing the paper
@article{tao2020rethinking,
title={Rethinking Motion Representation: Residual Frames with 3D ConvNets for Better Action Recognition},
author={Tao, Li and Wang, Xueting and Yamasaki, Toshihiko},
journal={arXiv preprint arXiv:2001.05661},
year={2020}
}
@article{tao2020motion,
title={Motion Representation Using Residual Frames with 3D CNN},
author={Tao, Li and Wang, Xueting and Yamasaki, Toshihiko},
journal={arXiv preprint arXiv:2006.13017},
year={2020}
}