Repository accompanying the "Sign Pose-based Transformer for Word-level Sign Language Recognition" paper

Related tags

Deep Learningtransformerslrlsa64sign-language-recognitionwlasl
Overview

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by Matyáš Boháček and Marek Hrúz, University of West Bohemia
Should you have any questions or inquiries, feel free to contact us here.

PWC

Repository accompanying the Sign Pose-based Transformer for Word-level Sign Language Recognition paper, where we present a novel architecture for word-level sign language recognition based on the Transformer model. We designed our solution with low computational cost in mind, since we see egreat potential in the usage of such recognition system on hand-held devices. We introduce multiple original augmentation techniques tailored for the task of sign language recognition and propose a unique normalization scheme based on sign language linguistics.

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Get Started

First, make sure to install all necessary dependencies using:

pip install -r requirements.txt

To train the model, simply specify the hyperparameters and run the following:

python -m train
  --experiment_name [str; name of the experiment to name the output logs and plots]
  
  --epochs [int; number of epochs]
  --lr [float; learning rate]
  
  --training_set_path [str; path to the csv file with training set's skeletal data]
  --validation_set_path [str; path to the csv file with validation set's skeletal data]
  --testing_set_path [str; path to the csv file with testing set's skeletal data]

If either the validation or testing sets' paths are left empty, these corresponding metrics will not be calculated. We also provide out-of-the box parameter to split the validation set as a desired split of the training set while preserving the label distribution for datasets without author-specified splits. These and many other specific hyperparameters with their descriptions can be found in the train.py file. All of them are provided a default value we found to be working well in our experiments.

Data

As SPOTER works on top of sequences of signers' skeletal data extracted from videos, we wanted to eliminate the computational demands of such annotation for each training run by pre-collecting this. For this reason and reproducibility, we are open-sourcing this data for WLASL100 and LSA64 datasets along with the repository. You can find the data here.

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License

The code is published under the Apache License 2.0 which allows for both academic and commercial use if relevant License and copyright notice is included, our work is cited and all changes are stated.

The accompanying skeletal data of the WLASL and LSA64 datasets used for experiments are, however, shared under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license allowing only for non-commercial usage.

Citation

If you find our work relevant, build upon it or compare your approaches with it, please cite our work as stated below:

@InProceedings{Bohacek_2022_WACV,
    author    = {Boh\'a\v{c}ek, Maty\'a\v{s} and Hr\'uz, Marek},
    title     = {Sign Pose-Based Transformer for Word-Level Sign Language Recognition},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops},
    month     = {January},
    year      = {2022},
    pages     = {182-191}
}
Comments
  • Pose based GRU model

    Pose based GRU model

    Thank you for providing this dataset. I'm trying to reproduce your results using the Pose based GRU model however I'm unable to do so. Could you please share the model architecture and hyperparameters. It would be quite helpful

    EDIT: Wrong repository, please delete this issue

    opened by farhaan-mukarram 0
  • Testing new data

    Testing new data

    I'm trying to use the model, but I'm having problems with the following step. I have already trained the model, and now I have the checkpoint_v_0.pth file, with which I do the following to load the generated model:

    model = torch.load(PATH/to/pth/file)
print(model)

    And this returns something like:

    SPOTER(
  (transformer): Transformer(
    (encoder): TransformerEncoder(
      (layers): ModuleList(
        (0): TransformerEncoderLayer(
          (self_attn): MultiheadAttention(
            (out_proj): _LinearWithBias(in_features=108, out_features=108, bias=True)
          )
          (linear1): Linear(in_features=108, out_features=2048, bias=True)
          (dropout): Dropout(p=0.1, inplace=False)
          (linear2): Linear(in_features=2048, out_features=108, bias=True)
          (norm1): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
          (norm2): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
          (dropout1): Dropout(p=0.1, inplace=False)
          (dropout2): Dropout(p=0.1, inplace=False)
        )
        (1): TransformerEncoderLayer(
...

    which makes me believe that everything is OK to this point.

    Now I would like to see how I can use the model to make a prediction with new data, and there's where the problem is.

    When running model(input) to get the results, some errors appear, and I believe that I'm not giving the correct kind of input. I'm using the second line from WLASL100_train_25fps.csv, changing the "s for [s in order to get a hierarchy like the following:

    [
           [a, b, c],
           [d],
           [e, f]
]

    However, this doesn't seem to work. Am I using a different format to the one the model should be given?

    The exact input I'm giving the model follows, with the np.array conversion:

    parsed_example = 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input = np.array([np.array(sublist) for sublist in parsed_example])
    opened by RodGal-2020 0
  • IndexError when training model

    IndexError when training model

    This is the command used to train the model : python -m train --experiment_name "Spoter" --training_set_path "data/WLASL100_train_25fps.csv" --validation_set_path "data/WLASL100_val_25fps.csv" --testing_set_path "data/WLASL100_test_25fps.csv"

    I get the following error after the program runs for awhile:

    Starting Spoter... Traceback (most recent call last): File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main "main", mod_spec) File "/usr/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) File "/content/drive/MyDrive/Spoter/train.py", line 272, in train(args) File "/content/drive/MyDrive/Spoter/train.py", line 174, in train train_loss, _, _, train_acc = train_epoch(slrt_model, train_loader, cel_criterion, sgd_optimizer, device) File "/content/drive/MyDrive/Spoter/spoter/utils.py", line 19, in train_epoch loss = criterion(outputs[0], labels[0]) File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/loss.py", line 1152, in forward label_smoothing=self.label_smoothing) File "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py", line 2846, in cross_entropy return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing) IndexError: Target 78 is out of bounds.

    Changing parameters like the epochs and learning rate does not fix the issue.

    question 
    opened by adhithiyaa-git 3
  • Problematic normalization

    Problematic normalization

    Screen Shot 2022-02-13 at 5 36 52 PM Got a validation accuracy around 58%, lower than the one proposed in the paper. Is the lower accuracy caused by this problematic normalization error?

    bug 
    opened by Coco-hanqi 6
  • Thank for your work! Please comment,when training ,report another error.

    Thank for your work! Please comment,when training ,report another error.

    RuntimeError: CUDA error: device-side assert triggered. ` for i, data in enumerate(dataloader): inputs, labels = data # inputs, labels = Variable(inputs), Variable(labels)-1 inputs = inputs.squeeze(0).to(device) labels = labels.to(device, dtype=torch.long)

        optimizer.zero_grad()
    outputs = model(inputs).expand(1, -1, -1)

    loss = criterion(outputs[0], labels[0])`
    bug 
    opened by showfaker66 5
Releases(supplementary-data)

  • supplementary-data(Dec 9, 2021)

    As SPOTER works on top of sequences of signers' skeletal data extracted from videos, we wanted to eliminate the computational demands of such annotation for each training run by pre-collecting this. For this reason and reproducibility, we are open-sourcing this data along with the code as well.

    This data is shared under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license allowing only for non-commercial usage only.

    We employed the WLASL100 and LSA64 datasets for our experiments. Their corresponding citations can be found below:

    @inproceedings{li2020word,
    title={Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison},
    author={Li, Dongxu and Rodriguez, Cristian and Yu, Xin and Li, Hongdong},
    booktitle={The IEEE Winter Conference on Applications of Computer Vision},
    pages={1459--1469},
    year={2020}
}

    @inproceedings{ronchetti2016lsa64,
    title={LSA64: an Argentinian sign language dataset},
    author={Ronchetti, Franco and Quiroga, Facundo and Estrebou, C{\'e}sar Armando and Lanzarini, Laura Cristina and Rosete, Alejandro},
    booktitle={XXII Congreso Argentino de Ciencias de la Computaci{\'o}n (CACIC 2016).},
    year={2016}
}
    Source code(tar.gz)
    Source code(zip)
    LSA64_60fps.csv(185.14 MB)
    WLASL100_test_25fps.csv(10.37 MB)
    WLASL100_train_25fps.csv(57.16 MB)
    WLASL100_val_25fps.csv(13.57 MB)
Owner
Matyáš Boháček
ML&NLP Researcher at @dataclair • Research Fellow with the University of West Bohemia •  WWDC19 & 21 Scholarship Winner
Matyáš Boháček
GitHub Repository https://spoter.signlanguagerecognition.com
Improving Compound Activity Classification via Deep Transfer and Representation Learning

Improving Compound Activity Classification via Deep Transfer and Representation Learning This repository is the official implementation of Improving C

NingLab 2 Nov 24, 2021
Cache Requests in Deta Bases and Echo them with Deta Micros

Deta Echo Cache Leverage the awesome Deta Micros and Deta Base to cache requests and echo them as needed. Stop worrying about slow public APIs or agre

Gingerbreadfork 8 Dec 07, 2021
3D dataset of humans Manipulating Objects in-the-Wild (MOW)

MOW dataset [Website] This repository maintains our 3D dataset of humans Manipulating Objects in-the-Wild (MOW). The dataset contains 512 images in th

Zhe Cao 28 Nov 06, 2022
Small-bets - Ergodic Experiment With Python

Ergodic Experiment Based on this video. Run this experiment with this command: p

Michael Brant 3 Jan 11, 2022
This repo contains the implementation of YOLOv2 in Keras with Tensorflow backend.

Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).

Huynh Ngoc Anh 1.7k Dec 24, 2022
Multi-task Learning of Order-Consistent Causal Graphs (NeuRIPs 2021)

Multi-task Learning of Order-Consistent Causal Graphs (NeuRIPs 2021) Authors: Xinshi Chen, Haoran Sun, Caleb Ellington, Eric Xing, Le Song Link to pap

Xinshi Chen 2 Dec 20, 2021
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs

Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs This is an implemetation of the paper Few-shot Relation Extraction via Baye

MilaGraph 36 Nov 22, 2022
MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images

MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images This repository contains the implementation of our paper MetaAvatar: Learni

sfwang 96 Dec 13, 2022
scalingscattering

Scaling The Scattering Transform : Deep Hybrid Networks This repository contains the experiments found in the paper: https://arxiv.org/abs/1703.08961

Edouard Oyallon 78 Dec 21, 2022
A fuzzing framework for SMT solvers

yinyang A fuzzing framework for SMT solvers. Given a set of seed SMT formulas, yinyang generates mutant formulas to stress-test SMT solvers. yinyang c

Project Yin-Yang for SMT Solver Testing 145 Jan 04, 2023
RaceBERT -- A transformer based model to predict race and ethnicty from names

RaceBERT -- A transformer based model to predict race and ethnicty from names Installation pip install racebert Using a virtual environment is highly

Prasanna Parasurama 3 Nov 02, 2022
Real-time pose estimation accelerated with NVIDIA TensorRT

trt_pose Want to detect hand poses? Check out the new trt_pose_hand project for real-time hand pose and gesture recognition! trt_pose is aimed at enab

NVIDIA AI IOT 803 Jan 06, 2023
PyTorch implementation of NeurIPS 2021 paper: "CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration"

CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration (NeurIPS 2021) PyTorch implementation of the paper: CoFiNet: Reli

76 Jan 03, 2023
李云龙二次元风格化!打滚卖萌，使用了animeGANv2进行了视频的风格迁移

李云龙二次元风格化！一键star、fork，你也可以生成这样的团长！ 打滚卖萌求star求fork! 0.效果展示 视频效果前往B站观看效果最佳：李云龙二次元风格化： github开源repo：李云龙二次元风格化 百度AIstudio开源地址,一键fork即可运行: 李云龙二次元风格化！一键fork

oukohou 44 Dec 04, 2022
Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" (NeurIPS'20)

IGNN Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" [paper] [supp] Prepare datasets 1 Download training dataset

Shangchen Zhou 278 Jan 03, 2023
Much faster than SORT(Simple Online and Realtime Tracking), a little worse than SORT

QSORT QSORT(Quick + Simple Online and Realtime Tracking) is a simple online and realtime tracking algorithm for 2D multiple object tracking in video s

Yonghye Kwon 8 Jul 27, 2022
IDA file loader for UF2, created for the DEFCON 29 hardware badge

UF2 Loader for IDA The DEFCON 29 badge uses the UF2 bootloader, which conveniently allows you to dump and flash the firmware over USB as a mass storag

Kevin Colley 6 Feb 08, 2022
OcclusionFusion: realtime dynamic 3D reconstruction based on single-view RGB-D

OcclusionFusion (CVPR'2022) Project Page | Paper | Video Overview This repository contains the code for the CVPR 2022 paper OcclusionFusion, where we

Wenbin Lin 193 Dec 15, 2022
codes for IKM (arXiv2021, Submitted to IEEE Trans)

Image-specific Convolutional Kernel Modulation for Single Image Super-resolution This repository is for IKM introduced in the following paper Yuanfei

Yuanfei Huang 9 Dec 29, 2022
Evaluating saliency methods on artificial data with different background types

Evaluating saliency methods on artificial data with different background types This repository contains the relevant code for the MedNeurips 2021 subm

2 Jul 05, 2022