Code for the paper A Flexible Model for Training Action Localization with Varying Levels of Supervision, NIPS 2018

Created by Jean-Baptiste Alayrac and Guilhem Chéron at INRIA, Paris.

Introduction

The webpage for this project is available here. It contains link to the paper, and other material about the work. This code reproduces the results presented in Table 1 of the paper for our method.

License

Our code is released under the MIT License (refer to the LICENSE file for details).

Cite

If you find this code useful in your research, please, consider citing our paper:

@InProceedings{actoraction18, author = {Ch'eron, Guilhem and Alayrac, Jean-Baptiste and Laptev, Ivan and Schmid, Cordelia}, title = {A Flexible Model for Training Action Localization with Varying Levels of Supervision}, booktitle = {Neural Information Processing Systems (NIPS)}, year = {2018} }

Contents

1. Requirements
2. Running the code

Requirements

We run the code under python 2.7 with the following dependencies:

• numpy
• tqdm
• mosek (for which you'll need a license)
• pickle
• scikit-learn
• scipy

Running

1. Clone this repo and go to the generated folder

git clone https://github.com/jalayrac/weak_action_loc.git
cd weak_action_loc

2. Download and unpack the preprocessed features needed for the desired experiment in the data folder:

• UCF101-24 (11 GiB)

  mkdir -p data
  cd data
  wget https://www.di.ens.fr/willow/research/weakactionloc/UCF101-24.tar.gz
  tar -xzvf UCF101-24.tar.gz
  cd ..

• DALY (48 GiB)

  mkdir -p data
  cd data
  wget https://www.di.ens.fr/willow/research/weakactionloc/DALY.tar.gz
  tar -xzvf DALY.tar.gz
  cd ..

3. To obtain the results, you need to first run the traning code to obtain the parameters of the model, and then run the evaluation code:

Dataset	Video level	Shot level	Temporal point	Temporal	Temporal + spatial points	1 BB	Temp. + 1 BB	Temp. + 3 BBs	Fully supervised
UCF101-24	script	-	script	script	script	script	script	script	script
DALY	script	script	-	script	-	script	script	script	-

NB: we provide the calibration files (threshold values) that were obtained by validation as described in the paper. We will release the code for this calibration in a future release.