This repository aims to collect and standardize results for various active learning algorithms for human pose estimation. While the repository contains official implementations for three algorithms: LearningLoss++, EGL++ and VL4Pose, we also include our code for various other algorithms that report results for human pose estimation. Further, we provide visualizations for various active learning algorithms to provide better insights into their sampling process!
- Create a new environment with
conda create --name AL4Pose python=3.8 - Activate this environment:
conda activate AL4Pose - Install via conda:
conda install -c pytorch -c conda-forge -c anaconda pytorch opencv albumentations matplotlib numpy umap-learn scipy scikit-learn scikit-image tensorboard pandas torchaudio torchvision pyyaml seaborn jupyter - Install via pip:
pip install adjusttext
The repository contains two main folders, codeand data. We have a separate folder cached which stores two files: Stacked_HG_ValidationImageNames.txt and mpii_cache_16jnts.npy. The former stores the names of those images which are used in the validation dataset in the original Stacked Hourglass paper (ECCV 2016), whereas the latter file stores the post-processed MPII dataset to avoid redundant processing every time the code is run.
Our framework currently supports two datasets: MPII and LSP/LSPET. These datasets can be downloaded from MPII Link, LSP Link and LSPET Link. For each of these datasets copy-paste all the images *.jpg into data/{mpii OR lsp OR lspet}/images/
├── LICENSE
├── README.md
├── cached
│ ├── Stacked_HG_ValidationImageNames.txt
│ └── mpii_cache_16jnts.npy
├── code
│ ├── activelearning.py
│ ├── activelearning_viz.py
│ ├── autograd_hacks.py
│ ├── config.py
│ ├── configuration.yml
│ ├── dataloader.py
│ ├── evaluation.py
│ ├── main.py
│ ├── models
│ │ ├── auxiliary
│ │ │ └── AuxiliaryNet.py
│ │ ├── hrnet
│ │ │ └── pose_hrnet.py
│ │ ├── learning_loss
│ │ │ └── LearningLoss.py
│ │ └── stacked_hourglass
│ │ ├── StackedHourglass.py
│ │ └── layers.py
│ └── utils.py
├── data
│ ├── lsp
│ │ ├── README.txt
│ │ ├── images
│ │ ├── joints.mat
│ │ └── lsp_filenames.txt
│ ├── lspet
│ │ ├── README.txt
│ │ ├── images
│ │ ├── joints.mat
│ │ └── lspet_filenames.txt
│ └── mpii
│ ├── README.md
│ ├── images
│ ├── joints.mat
│ └── mpii_filenames.txt
└── resultsRunning python main.py in the code folder executes the code, with configurations specified in configuration.yml
The following algorithms are implemented which have also reported results for human pose estimation (alphabetical order):
- Aleatoric Uncertainty
We use an extension of Kendall and Gal's NeurIPS 2017 paper What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? for human pose estimation. Our implementation includes computing the argmax of the heatmap for each joint to obtain a two dimensional coordinate. This allows us to directly apply the formulation in the original paper to human pose estimation.
- Core-Set
Sener and Savarese in their ICML 2018 Active Learning for Convolutional Neural Networks: A Core-Set Approach provided theoretical results to support Core-Set for deep neural networks. In our implementation we perform pooling over the penultimate layer to obtain a vector encoding for each image and subsequently use it for k-Centre Greedy.
- EGL++
In our WACV 2022 Bayesian Uncertainty and Expected Gradient Length - Regression: Two Sides of the Same Coin?, we show that computing the expected gradient length a.k.a EGL (an active learning algorithm) is equivalent to estimating Bayesian uncertainty in computer vision. Further, we propose a modification EGL++ that extends EGL to human pose estimation. NOTE: PyTorch no longer supports register_backward_hook() so future versions of PyTorch (possibly after 1.13) may require modification of code.
- LearningLoss++ / Learning Loss
Yoo and Kweon in their CVPR 2019 paper proposed a method, Learning Loss, for learning the possible 'loss' for an input sample, and showcased results for classification, object detection and pose estimation. In a subsequent improvement, our work A Mathematical Analysis of Learning Loss for Active Learning in Regression provides theoretical insights into Learning Loss and provides a new framework, LearningLoss++, using these insights to modify Learning Loss. LearningLoss++ reports results for human pose estimation.
- Multi-Peak Entropy
Amongst the earliest algorithms, Multi-Peak Entropy (ICCV 2017) leveraged spatial ambiguities in the heatmap to identify images for active learning. We expect that confident predictions will have a unimodal heatmap whereas images containing joint level ambiguities tend to have multiple modes. Multi-Peak entropy uses these ambiguities to measure entropy across various modes of the heatmap.
- VL4Pose (Visual Likelihood for Pose Estimation)
In our BMVC 2022 paper (VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation) we propose VL4Pose; an algorithm that utilizes simple domain knowledge to unify joint and pose level uncertainty and perform fast yet reliable active learning, out-of-distribution and pose refinement (to a limited extent). VL4Pose does this by training an auxiliary network to maximize the likelihood of training poses. As a consequence, out-of-distribution poses incur a low likelihood in our framework making them suitable candidates for active learning.
The code needs to be run each time for every active learning cycle and for every algorithm separately. All active learning algorithms require a pretrained model, which is the base model for subsequent active learning cycles. A sample configuration file to create this base model is available in sample_configs/base_model.yml. For active learning algorithms such as Multi-Peak entropy and Core-Set which do not use an auxiliary model, we re-run the code with configuration: /sample_configs/non_auxiliary.yml. For active learning algorithms which require an auxiliary network (LearningLoss++, VL4Pose, Aleatoric), we need to load the base model and train the auxiliary network with objective specific to the algorithm. This configuration is available in sample_configs/train_auxiliary_from_base.yml. Now that we have a trained auxiliary network corresponding to the base model, we can perform active learning these methods using the configuration sample_configs/auxiliary.yml. In fact, for subsequent active learning algorithms we don't need to run the code separately to train the auxiliary network since the new configuration explicitly specifies training both: the pose estimator and the auxiliary network (gradients stopped from flowing into the pose estimator) simultaneously.
Visualization: Once we have a pretrained model (and auxiliary model if applicable), it is possible to visualize the sampling process of various active learning algorithms by simply setting activelearning_visualization: True (line 48 in configuration.yml).
This code has been used to support the following three of our works:
- "VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation", BMVC 2022
- "Bayesian Uncertainty and Expected Gradient Length - Regression: Two Sides Of The Same Coin?", WACV 2022
- "A Mathematical Analysis Of Learning Loss For Active Learning In Regression", CVPR-W 2021
If you found this code useful, please consider citing all three publications (it doesn't cost anything) :D Also, feedback is welcome!
Please contact me at megh.shukla [at] epfl.ch
@inproceedings{Shukla_2022_BMVC,
author = {Megh Shukla and Roshan Roy and Pankaj Singh and Shuaib Ahmed and Alexandre Alahi},
title = {VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/0610.pdf}
}
@INPROCEEDINGS{9706805,
author={Shukla, Megh},
booktitle={2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
title={Bayesian Uncertainty and Expected Gradient Length - Regression: Two Sides Of The Same Coin?},
year={2022},
volume={},
number={},
pages={2021-2030},
doi={10.1109/WACV51458.2022.00208}}
@INPROCEEDINGS{9523037,
author={Shukla, Megh and Ahmed, Shuaib},
booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
title={A Mathematical Analysis of Learning Loss for Active Learning in Regression},
year={2021},
volume={},
number={},
pages={3315-3323},
doi={10.1109/CVPRW53098.2021.00370}}
I am a first year Ph.D. student in Electrical Engineering at EPFL supervised by Prof. Alexandre Alahi, Visual Intelligence for Transportation Lab . My research interests lie in uncertainty estimation, active learning and probabilistic modelling for keypoint estimation. These interests are peppered throughout my fledgling career, at Mercedes-Benz and now at EPFL. As a computer vision research engineer, I lead R&D in active learning, an academic research area with tangible business benefits. I take pride in not only providing theoretical and applied advancements in active learning [BMVC22, WACV22, CVPRW21], but also in engineering my research into the project's data pipelines. Taking research beyond publications and into production allowed me a holistic view of the research and development cycle which remains a defining moment in my career.