Action Sets: Weakly Supervised Action Segmentation without Ordering Constraints
Code for the paper Action Sets: Weakly Supervised Action Segmentation without Ordering Constraints
- download the data from https://uni-bonn.sciebo.de/s/vVexqxzKFc6lYJx
- extract it so that you have the
datafolder in the same directory as
- create a
resultsdirectory in the same directory where you also find
Requirements: Python2.7 with the libraries numpy, pytorch, and scipy
./main.py inference --n_threads=NUM_THREADS, where
NUM_THREADS should be replaced with the number of parallel CPU threads you want to use for Viterbi decoding.
In the inference step, recognition files are written to the
results directory. The frame-level ground truth is available in
./eval.py --recog_dir=results --ground_truth_dir=data/groundTruth to evaluate the frame accuracy of the trained model
We provide a python/pytorch implementation for easy usage. In the paper, we used an internal C++ implementation, so results can be slightly different. Running the provided setup on split1 of Breakfast should lead to roughly 23% frame accuracy.
If you use the code, please cite
A. Richard, H. Kuehne, J. Gall: Action Sets: Weakly Supervised Action Segmentation without Ordering Constraints in IEEE Int. Conf. on Computer Vision and Pattern Recognition, 2018