anticipating-activities's Introduction

When will you do what? - Anticipating Temporal Occurrences of Activities

This repository provides a TensorFlow implementation of the paper When will you do what? - Anticipating Temporal Occurrences of Activities.

Qualitative Results:

Click on the image.

Training:

download the data from https://uni-bonn.sciebo.de/s/3Wyqu3cxYSm47Kg.
extract it so that you have the data folder in the same directory as main.py.
To train the model on split1 of Breakfast dataset run python main.py --model=MODEL --action=train --vid_list_file=./data/train.split1.bundle where MODEL is cnn or rnn.
To change the default saving directory or the model parameters, check the list of options by running python main.py -h.

Prediction:

Run python main.py --model=MODEL --action=predict --vid_list_file=./data/test.split1.bundle for evaluating the the model on split1 of Breakfast.
To predict from ground truth observation set --input_type option to gt.
To check the list of options run python main.py -h.

Evaluation:

Run python eval.py --obs_perc=OBS-PERC --recog_dir=RESULTS-DIR. Where RESULTS-DIR contains the output predictions for a specific observation and prediction percentage, and OBS-PERC is the corresponding observation percentage. For example python eval.py --obs_perc=.3 --recog_dir=./save_dir/results/rnn/obs0.3-pred0.5 will evaluate the output corresponding to 0.3 observation and 0.5 prediction.

Remarks:

If you use the code, please cite

Y. Abu Farha, A. Richard, J. Gall:
When will you do what? - Anticipating Temporal Occurrences of Activities
in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018

To download the used features please visit: An end-to-end generative framework for video segmentation and recognition.

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