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[TPAMI] A Unified Multimodal De- and Re-coupling Framework for RGB-D Motion Recognition

<a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a> <a href="https://iplab.dmi.unict.it/MECCANO/challenge.html"><img alt="4th Place" src="https://img.shields.io/badge/ICIAP Multimodal Action Recognition Competition-4th Place-color?logo=trophy&logoColor=white"></a>

This repo is the official implementation of "A Unified Multimodal De- and Re-coupling Framework for RGB-D Motion Recognition" as well as the follow-ups. It is an extension of the CVPR'23 paper. It currently includes code and models for the following tasks:

RGB-D-based Action Recognition: Included in this repo.

RGB-D-based Gesture Recognition: Included in this repo.

Video data augementation: Included in this repo. See the ShuffleMix+ strategy in this paper.

News

2024/2/28

1. To reuse UMDR-Net on a new action recognition dataset, one may refer to this guide.

2023/10/06

1. This method achieved the 4th Place in the ICIAP Multimodal Action Recognition Competition.

2023/07/29

1. Uploaded the training results on the NTU-RGBD-120 dataset.

2023/06/20

1. Add dataset split files.
2. Fixed some bugs.
3. Update README.

Attention Visualization

• Visualization of the class activation responses for RGB and depth modalities.

1. Performance

The proposed method (UMDR) outperforms a number of state-of-the-art methods on both action and gesture datasets.

2. Requirements

This is a PyTorch implementation of our paper. torch>=1.7.0; torchvision>=0.8.0; Visdom(optional)

Data prepare: dataset with the following folder structure:

│NTURGBD/
├──dataset_splits/
│  ├── @CS
│  │   ├── train.txt
                video name               total frames    label
│  │   │    ├──S001C001P001R001A001_rgb      103          0 
│  │   │    ├──S001C001P001R001A004_rgb      99           3 
│  │   │    ├──...... 
│  │   ├── valid.txt
│  ├── @CV
│  │   ├── train.txt
│  │   ├── valid.txt
├──ImagesResize/
│  │   ├── S001C002P001R001A002_rgb
│  │   │   ├──000000.jpg
│  │   │   ├──000001.jpg
│  │   │   ├──......
├──nturgb+d_depth_masked/
│  │   ├── S001C002P001R001A002
│  │   │   ├──MDepth-00000000.png
│  │   │   ├──MDepth-00000001.png
│  │   │   ├──......

NOTE: We use the NTU dataset’s high-resolution RGB video (1280x960). To avoid losing infoemation, we do not resize the video frames directly to 320x240. Instead, we crop a 640x480 ROI area for each frame using the realeased mask images. Then we resize the cropped area to 320x240 for training and testing. See data/data_preprose_for_NTU.py for the data preprocessing codes.

3. Methodology

4. Train and Evaluate

We pre-trained all of our models on the 20BN Jester V1 dataset, except for NTU-RGBD. Alternatively, one can use the parameters trained on NTU-RGBD to initialize the model before training on other datasets, such as IsoGD, NvGesture and THU-READ.

Unimodal Training

Take training an RGB model with 8 GPUs on the NTU-RGBD dataset as an example,

# type: M(rgb), K(depth); sample-duration: the length of the video clip;  smprob: hyperparameter  $\rho$; mixup: hyperparameter  $\alpha_{m}$; shufflemix: $\alpha_{s}$; intar-fatcer: Controls the temporal resolution of each sub-branch in DTN (default: set 2 when sample-duration=16/32;  set 4 when sample-duration=64).

python -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 --use_env train.py --config config/NTU.yml --data /path/to/Dataset/NTU-RGBD/frames --splits /path/to/Dataset/NTU-RGBD/dataset_splits/@CS/ --save ./output_dir/ --batch-size 16  --sample-duration 32 \
--opt sgd \
--lr 0.01 \
--sched cosine \
--smprob 0.2 --mixup 0.8 --shufflemix 0.3 --epochs 100 --distill 0.2 --type M --intar-fatcer 2 

Take training an RGB model with 8 GPUs on the IsoGD dataset as an example,

python -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 --use_env train.py --config config/IsoGD.yml --data /path/to/Dataset/IsoGD/frames --splits ./data/dataset_splits/IsoGD/rgb/ --save ./output_dir/ --batch-size 16  --sample-duration 32 \
--opt sgd \
--lr 0.01 \
--sched cosine \
--smprob 0.2 --mixup 0.8 --shufflemix 0.3 --epochs 100 --distill 0.2 --type M --intar-fatcer 2 \
--finetune ./Checkpoints/NTU-RGBD-32-DTNV2-TSM/model_best.pth.tar

Cross-modal Fusion

# scc-depth: number of CFCer used in spatial domain. tcc-depth: number of CFCer used in temporal domain.
python -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 --use_env train_fusion.py --config config/NTU.yml --data /path/to/Dataset/NTU-RGBD/frames --splits /path/to/Dataset/NTU-RGBD/dataset_splits/@CS/ --save ./output_dir/ --batch-size 16 --sample-duration 32 \
--smprob 0.2 --mixup 0.8 --shufflemix 0.3 --epochs 100 --distill 0.0 --intar-fatcer 2 \
--FusionNet cs32 --lr 0.01 --sched step --opt sgd --decay-epochs 10 --scc-depth 2 --tcc-depth 4 --type rgbd

Score Fusion

python -m torch.distributed.launch --nproc_per_node=1 --master_port=1234 --use_env score_fusion.py --config config/NTU.yml --data /path/to/Dataset/NTU-RGBD/frames --splits /path/to/Dataset/NTU-RGBD/dataset_splits/@CS/ --save ./output_dir/ --batch-size 16 --sample-duration 32  --intar-fatcer 2 --FusionNet cs32 --type rgbd

Evaluation

python -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 --use_env train.py --config config/NTU.yml --data /path/to/Dataset/NTU-RGBD/frames --splits /path/to/Dataset/NTU-RGBD/dataset_splits/@CS/  --batch-size 16 --sample-duration 32 --eval_only --resume /path/to/model_best.pth.tar 

5. Models Download

Citation

@ARTICLE{zhou2023umdr,
  author={Zhou, Benjia and Wang, Pichao and Wan, Jun and Liang, Yanyan and Wang, Fan},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={A Unified Multimodal De- and Re-Coupling Framework for RGB-D Motion Recognition}, 
  year={2023},
  pages={1-15},
  doi={10.1109/TPAMI.2023.3274783}}

LICENSE

The code is released under the MIT license.

Copyright

Copyright (C) 2010-2021 Alibaba Group Holding Limited.