He Zhao1,2, Isma Hadji1, Nikita Dvornik1, Konstantinos G. Derpanis1,2, Richard P. Wildes1,2, Allan D. Jepson1,
1Samsung AI Centre (SAIC) Toronto 2York University
This research was conducted while He was an intern at SAIC-Toronto and is funded by Samsung Research.
Abstract: In this paper, we study the problem of procedure planning in instructional videos. Here, an agent must produce a plausible sequence of actions that can transform the environment from a given start to a desired goal state. When learning procedure planning from instructional videos, most recent work leverages intermediate visual observations as supervision, which requires expensive annotation efforts to localize precisely all the instructional steps in training videos. In contrast, we remove the need for expensive temporal video annotations and propose a weakly supervised approach by learning from natural language instructions. Our model is based on a transformer equipped with a memory module, which maps the start and goal observations to a sequence of plausible actions. Furthermore, we augment our model with a probabilistic generative module to capture the uncertainty inherent to procedure planning, an aspect largely overlooked by previous work. We evaluate our model on three datasets and show our weakly-supervised approach outperforms previous fully supervised state-of-the-art models on multiple metrics.
The following sections contain PyTorch code for running our approach on three datasets reported in this paper: CrossTask [1], COIN [2] and NIV [3]. For each dataset, you can choose between: (i) Using the prepared video features. (ii) Extracting features from scratch (e.g., from raw videos). The second option allows for testing our approach on arbitrary datasets. The {dataset}_main.py file for each datasets contains both train and evaluation code, controlled by a hyper-parameter under if __name__ == __main__ function.
conda create --channel conda-forge --name procedureFormer python=3.7.3conda activate procedureFormerconda install --file requirements.txt
This code assumes CUDA support.
(i) Set-up Dataset. We provide two ways to step-up the dataset for CrossTask [1]. You can either use pre-extracted features
cd datasets/CrossTask_assets
wget https://www.di.ens.fr/~dzhukov/crosstask/crosstask_release.zip
wget https://www.di.ens.fr/~dzhukov/crosstask/crosstask_features.zip
wget https://vision.eecs.yorku.ca/WebShare/CrossTask_s3d.zip
unzip '*.zip'
or extract features from raw video using the following code (* Both options work, pick one to use)
cd raw_data_process
python download_CrossTask_videos.py
python InstVids2TFRecord_CrossTask.py
bash lmdb_encode_CrossTask.sh 1 1
(ii) Train and Evaluation. Set the hyper-variable train in CrossTask_main.py (i.e., the one under if __name__ == __main__) to either True or False, to choose between training a network or evaluating a pre-trained model. By default, the code will load the used random datasplit (see datasplit.pth in ./checkpoints) as well as our pre-trained weights (included in ./checkpoints folder).
# Set 'train' to (True, False) and then
python CrossTask_main.py
(iii) Results reproduced from pre-trained model (Numbers may vary from runs to runs, due to probalistic sampling)
| Prediction Horizon T = 3 | Success Rate | mean Accuracy | mIoU |
|---|---|---|---|
| Viterbi | 23.40 | 52.71 | 73.31 |
| Argmax | 22.27 | 52.64 | 73.28 |
(i) Set-up Dataset. Similarly, to use COIN dataset [2] on our approach, we provide pre-extracted features
cd datasets/CrossTask_assets
wget https://vision.eecs.yorku.ca/WebShare/COIN_s3d.zip
unzip '*.zip'
or we support extracting features from raw video
cd raw_data_process
python download_COIN_videos.py
python InstVids2TFRecord_COIN.py
bash lmdb_encode_COIN.sh 1 1
(ii) Train and Evaluation. The train/evaluation code for COIN is in the same design as before.
python COIN_main.py
(iii) Results reproduced from pre-trained model. Note that figures in below table are slightly higher than those reported in the paper, as this table comes from one random split and paper reports averaged results from five random split.
| Prediction Horizon T = 3 | Success Rate | mean Accuracy | mIoU |
|---|---|---|---|
| Viterbi | 16.61 | 25.76 | 73.48 |
| Argmax | 14.05 | 25.82 | 73.14 |
(i) Set-up Dataset. For the NIV dataset [3], either use pre-extracted features
cd datasets/NIV_assets
wget https://vision.eecs.yorku.ca/WebShare/NIV_s3d.zip
unzip '*.zip'
or extract features from raw video by first downloading videos from official project page
cd datasets/NIV_assets/videos
wget https://www.di.ens.fr/willow/research/instructionvideos/data_new.tar.gz
tar -xvzf data_new.tar.gz
find ./data_new -type f -name “*.mpg” | xargs -iF mv F .
and then jump to raw_data_process and process raw videos
cd raw_data_process
python InstVids2TFRecord_NIV.py
bash lmdb_encode_NIV.sh 1 1
(ii) Train and Evaluation. The train/evaluation code for NIV is in the same design as before.
python NIV_main.py
(iii) Results reproduced from pre-trained model
| Prediction Horizon T = 3 | Success Rate | mean Accuracy | mIoU |
|---|---|---|---|
| Viterbi | 24.02 | 47.18 | 71.15 |
| Argmax | 15.32 | 43.84 | 71.05 |
If you find this code useful in your work then please cite
@inproceedings{he2022p3iv,
title={P3IV: Probabilistic Procedure Planning from Instructional Videos with Weak Supervision},
author={He, Zhao and Hadji, Isma and Nikita, Dvornik and Konstantinos, G., Derpanis and Richard, P., Wildes and Allan, D., Jepson},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
month = {June.},
year={2022}
}Please contact He Zhao @ [email protected] if any issue.
[1] D. Zhukov et al. "Cross-task weakly supervised learning from instructional videos." CVPR'19.
[2] Y. Tang et al. "COIN: A large-scale dataset for comprehensive instructional video analysis." CVPR'19
[3] JB. Alayrac et al. "Unsupervised learning from narrated instruction videos." CVPR'16.
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