In the CVPR'18 paper, Table 3 uses version 0 of the CharadesEgo dataset for evaluation. Updated table for version 1 of the dataset will be added here.

From: Actor and Observer: Joint Modeling of First and Third-Person Videos, CVPR 2018

Contributor: Gunnar Atli Sigurdsson

• This code implements a triplet network in PyTorch

The code implements found in:

@inproceedings{sigurdsson2018actor,
author = {Gunnar A. Sigurdsson and Abhinav Gupta and Cordelia Schmid and Ali Farhadi and Karteek Alahari},
title = {Actor and Observer: Joint Modeling of First and Third-Person Videos},
booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2018},
code = {https://github.com/gsig/actor-observer},
}

The Charades-Ego and Charades datasets are available at https://allenai.org/plato/charades/

All outputs are stored in the cache-dir. This includes epoch*.txt which is the classification output. All output files can be scored with the official MATLAB evaluation script provided with the Charades / CharadesEgo datasets.

Requirements:

• Python 2.7
• PyTorch

1. Download the CharadesEgo Annotations (allenai.org/plato/charades/)
2. Download the CharadesEgo RGB frames (allenai.org/plato/charades/)
3. Duplicate and edit one of the experiment files under exp/ with appropriate parameters. For additional parameters, see opts.py
4. Run an experiment by calling python exp/rgbnet.py where rgbnet.py is your experiment file
5. The checkpoints/logfiles/outputs are stored in your specified cache directory.
6. Build of the code, cite our papers, and say hi to us at CVPR.

Good luck!

Charades submission files are available for multiple baselines at https://github.com/gsig/temporal-fields