Self-supervised Object-Centric Learning for Videos

Paper | Webpage

This is the official implementation of the paper Self-supervised Object-Centric Learning for Videos published in NeurIPS 2023.

Introduction

1. Clone this repository:

git clone https://github.com/gorkaydemir/SOLV.git
cd SOLV

2. Create a conda environment and install the dependencies:

conda create -n SOLV python=3.9
conda activate SOLV
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
pip install -r requirements.txt

3. Install YoutubeVIS 2019 dataset to /path/to/root

Training

torchrun --master_port=12345 --nproc_per_node=#gpus train.py \
--root /path/to/root \
--model_save_path /path/to/checkpoint_dir

Inference

torchrun --master_port=12345 --nproc_per_node=1 train.py \
--root /path/to/root \
--model_save_path /path/to/checkpoint_dir \
--checkpoint_path /path/to/checkpoint_dir/checkpoint.pt --use_checkpoint --validate

To-do

• Add DAVIS-17 finetuning and evaluation code

How to Cite

@InProceedings{Aydemir2023NeurIPS,
    author = {Aydemir, G\"orkay and Xie, Weidi and G\"uney, Fatma},
    title = {{S}elf-supervised {O}bject-centric {L}earning for {V}ideos},
    booktitle = {Advances in Neural Information Processing Systems},
    year      = {2023}}

Acknowledgments

I would like to thank Merve Rabia Barin for validating and reproducing the results using this repository.