Code for the paper Action Sets: Weakly Supervised Action Segmentation without Ordering Constraints
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utils
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README.md
eval.py
main.py

README.md

Action Sets: Weakly Supervised Action Segmentation without Ordering Constraints

Code for the paper Action Sets: Weakly Supervised Action Segmentation without Ordering Constraints

Prepraration:

  • download the data from https://uni-bonn.sciebo.de/s/vVexqxzKFc6lYJx
  • extract it so that you have the data folder in the same directory as main.py
  • create a results directory in the same directory where you also find main.py: mkdir results

Requirements: Python2.7 with the libraries numpy, pytorch, and scipy

Training:

Run ./main.py training

Inference:

Run ./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.

Evaluation:

In the inference step, recognition files are written to the results directory. The frame-level ground truth is available in data/groundTruth. Run ./eval.py --recog_dir=results --ground_truth_dir=data/groundTruth to evaluate the frame accuracy of the trained model

Remarks:

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