Video action detection requires annotations at every frame, which drastically increases the labeling cost. In this work, we focus on efficient labeling of videos for action detection to minimize this cost. We propose active sparse labeling (ASL), a novel active learning strategy for video action detection.
Visit the project page HERE for more details.
This is an implementation for the NeurIPS 2022 paper titled: Are All Frames Equal? Active Sparse Labeling for Video Action Detection.
- python >= 3.6
- pytorch >= 1.6
- numpy >= 1.19
- scipy >= 1.5
- opencv >= 3.4
- scikit-image >= 0.17
- scikit-learn >= 0.23
- tensorboard >= 2.3
We developed our code base on Ubuntu 18.04 using anaconda3. We suggest to clone our anaconda environment using the following code:
$ conda create --name <env> --file spec-file.txt
The code expects UCF101 dataset in data/UCF101 folder (same format as direct download from source).
To use pretrained weights, please download charades pretrained i3d weights into weights folder from given link: https://github.com/piergiaj/pytorch-i3d/blob/master/models/rgb_charades.pt
The trained models will be saved under trained/active_learning/checkpoints_ucf101_capsules_i3d folder
The labels/annotations for ucf101 is saved as pickle files for easier processing.
To train, place the data and weights in appropriate folder. Then run as
python3 train_ucf101_capsules.py <percent>
This will use the APU algorithm to select frames and create new annotation pickle file. Run as:
python3 APU.py