RareAct

This repository contains annotation for the RareAct dataset as well as an evaluation script for computing the wAP and sAP metrics described in the paper.

Requirements (only for the evaluation script)

• Python 3
• Pandas
• Scikit-learn

Data

You can download the videos zipped into one file here. The video names are the YouTube ids of the videos.

The annotation file is hosted in the github repo and named as rareact.csv

Here is a description of each column:

Column Name	Type	Example	Description
id	int	14	Unique ID for the annotated video segment.
video_id	string	7frRY7aGwMU	YouTube ID of the video where the segment originated from (unique per video).
start	int	3	Start time in seconds of the action segment.
end	int	5	End time in seconds of the action segment.
class_id	int [0-148]	8	The class identifier of the actions (verb, noun). Maximum id: 148.
verb	string	cut	Action verb describing the interaction.
noun	string	laptop	Object noun subject of the interaction.
annotation	int [0-4]	1	Annotation for the given clip and (verb, noun) class. 1: Positive. 2: Hard negative (only verb is right): 3: Hard negative (only noun is right). 4: Hard negative (Both verb and noun are valid but verb is not applied to noun). 0: Negative.

Evaluation script

We provide an evaluation python script. To run an evaluation you need first to create a prediction output numpy matrix of shape 7607x149. where each row represent the samples ordered similarly as in rareact.csv and each column is the prediction score for each of the action class_id.

To compute the mWAP just run:

python compute_score.py predictions.npy 

To compute the mSAP (n=100) just run:

python compute_score.py predictions.npy 100 

where predictions.npy is the prediction output numpy array as described above.

References

If you find this dataset useful, please cite the following paper:

@article{miech20rareact,
   title={RareAct: A video dataset of unusual interactions},
   author={Miech, Antoine and Alayrac, Jean-Baptiste and Laptev, Ivan and Sivic, Josef and Zisserman, Andrew},
   journal={arxiv:2008.01018},
   year={2020},
}