Code for the paper NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learning

• 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 train.py
• create a results directory in the same directory where you also find train.py: mkdir results

The data for 50Salads is also available online: https://uni-bonn.sciebo.de/s/MUdKRA3xLhrW2ZE

Requirements: Python3.x with the libraries numpy and pytorch (version 0.4.1)

Run python3 train.py

Run python3 infernece.py Note: adjust the variable n_threads in 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 data/groundTruth. Run 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