NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learning
Code for the paper NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learning
- download the data from https://uni-bonn.sciebo.de/s/wOxTiWe5kfeY4Vd
- 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: Python3.x with the libraries numpy and pytorch (version > 0.4.1)
Note: adjust the variable
inference.py to your needs.
In the inference step, recognition files are written to the
results directory. The frame-level ground truth is available in
python 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 a faster in-house C++ implementation, so results can be slightly different. Running the provided setup on split1 of Breakfast should lead to roughly 42% frame accuracy.
If you use the code, please cite
A. Richard, H. Kuehne, A. Iqbal, J. Gall: NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learning in IEEE Int. Conf. on Computer Vision and Pattern Recognition, 2018