Introduction

Official code of the CVPR 2020 paper 

Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications

available on arxiv.

Summary

Learn a video representation that can generalize to unseen actions. Semantic information are used as supervision. In particular, the visual representation is mapped into the Word2Vec embedding, where words semantically related are closer to each other in an euclidean sense.

Checkpoints

The trained models, used to produce the numbers in the paper, can be downloaded here.

Install

Requirements

Run install.sh to get the uncommon libraries (faiss, tensorboardx, joblib) and the latest version of pytorch compatible with cuda 9.2 installed in the docker.

Retrieve external assets

Get the word2vec model

sudo chmod + assets/download_word2vec.sh
./assets/download_word2vec.sh

Get C3D pretrained model

wget http://imagelab.ing.unimore.it/files/c3d_pytorch/c3d.pickle -P /workplace/

Running

The script run.sh shows an example of parameters for starting the training of the e2e model.

Training

get_dataset

In case you want to train your model on Kinetics, you need to adapt the function get_kinetics() in auxiliary/auxuliary_dataset.py according to the format in which Kinetics is stored on your machine. The current version is just a placeholder and will NOT work right away.

train on Kinetics, test on [UCF101, HMDB51]. End2End mode

python3 main.py --n_epochs 150 --bs 22 --lr 1e-3 --network r2plus1d_18 --dataset kinetics2both --save_path PATH_TO_RESULT_FOLDER --nopretrained

train on Kinetics, test on [UCF101, HMDB51, ActivityNet]. End2End mode

python3 main.py --n_epochs 150 --bs 22 --lr 1e-3 --network r2plus1d_18 --dataset kinetics2others --save_path PATH_TO_RESULT_FOLDER --nopretrained

train on Kinetics, test on [UCF101, HMDB51]. Baseline mode

python3 main.py --n_epochs 150 --bs 22 --lr 1e-3 --network r2plus1d_18 --dataset kinetics2both --save_path PATH_TO_RESULT_FOLDER --fixed

train on Kinetics, test on [UCF101, HMDB51, ActivityNet]. Baseline mode

python3 main.py --n_epochs 150 --bs 22 --lr 1e-3 --network r2plus1d_18 --dataset kinetics2others --save_path PATH_TO_RESULT_FOLDER --fixed

train on Kinetics, test on [UCF101, HMDB51]. End2End mode pretrained on SUN

python3 main.py --n_epochs 150 --bs 22 --lr 1e-3 --network r2plus1d_18 --dataset kinetics2both --save_path PATH_TO_RESULT_FOLDER --weights [path_to_SUN_pretraining]

train on Kinetics, test on [UCF101, HMDB51, ActivityNet]. End2End mode pretrained on SUN

python3 main.py --n_epochs 150 --bs 22 --lr 1e-3 --network r2plus1d_18 --dataset kinetics2others --save_path PATH_TO_RESULT_FOLDER --weights [path_to_SUN_pretraining]