Masked Event Modelling: Self-Supervised Pretraining for Event Cameras

Masked Event Modelling introduces pretraining on raw event camera data without requiring additional labels. It is joint work by

Simon Klenk^{*, 1}   David Bonello^{*, 1}   Lukas Koestler^{*, 1}   Nikita Araslanov¹   Daniel Cremers¹  

¹Computer Vision Group, Technical University of Munich, Germany
  ^* Equal Contribution.  

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2024.

Link to paper: arXiv

Event cameras asynchronously capture brightness changes with low latency, high temporal resolution, and high dynamic range. However, annotation of event data is a costly and laborious process, which limits the use of deep learning methods for classification and other semantic tasks with the event modality. To reduce the dependency on labeled event data, we introduce Masked Event Modeling (MEM), a self-supervised framework for events. Our method pretrains a neural network on unlabeled events, which can originate from any event camera recording. Subsequently, the pretrained model is finetuned on a downstream task, leading to a consistent improvement of the task accuracy. For example, our method reaches state-of-the-art classification accuracy across three datasets, N-ImageNet, N-Cars, and N-Caltech101, increasing the top-1 accuracy of previous work by significant margins. When tested on real-world event data, MEM is even superior to supervised RGB-based pretraining. The models pretrained with MEM are also label-efficient and generalize well to the dense task of semantic image segmentation.

Citation:

@inproceedings{masked-event-modeling,
      title={Masked Event Modeling: Self-Supervised Pretraining for Event Cameras}, 
      author={Simon Klenk, David Bonello, Lukas Koestler, Nikita Araslanov and Daniel Cremers},
      booktitle=WACV,
      year={2024}
}

Setup

First, create the project directory

mkdir masked-event-modelling
cd masked-event-modelling

Clone the repo

git clone https://github.com/tum-vision/mem
cd mem

Install miniconda (https://docs.conda.io/en/latest/miniconda.html) and set up the environment

conda create --name mem python=3.10
conda activate mem

Install pytorch (https://pytorch.org/get-started/locally/)

Install Requirements

pip install -r requirements.txt

Datasets

Install an event dataset. Here we will install N-Caltech101.

Create a directory for the datasets in the project directory

cd ..
mkdir datasets
cd datasets

Install N-Caltech101, unzip it and extract the files

wget -O ncaltech101-raw.zip https://prod-dcd-datasets-cache-zipfiles.s3.eu-west-1.amazonaws.com/cy6cvx3ryv-1.zip
unzip ncaltech101-raw.zip
unzip cy6cvx3ryv-1/Caltech101.zip
rm ncaltech101-raw.zip
rm -r cy6cvx3ryv-1

Preprocess the dataset

python ../mem/process_data/process_dataset.py --dataset ncaltech101 --cores 4 --input Caltech101 --output ncaltech101 --split ../mem/configs/splits/ncaltech101.txt

Removew the raw dataset

rm -r Caltech101

Logging

Logging is done with Weights & Biases. Log in with your account.

wandb login

Checkpoints

A pretrained checkpoint on the N-ImageNet dataset can be downloaded here.

Training

Training can be done in one of two ways: locally or on a slurm cluster.

The pipeline sets up a new directory with the config files and scripts needed to run the experiment. It then submits the experiment to a slurm cluster if wanted.

To train with the pipeline, create a config file (like the ones in mem/configs) and run one of the following commands while in the mem directory

Local training

./run-pipeline.sh ./configs/ncaltech.conf

Training on SLURM cluster

cd mem
./run-pipeline.sh ./configs/ncaltech.conf slurm

Semantic Segmentation

Follow the README in mem/semantic_segmentation to train a semantic segmentation model.

Attribution

• This code is based on Microsoft's BEiT code (https://github.com/microsoft/unilm/tree/master/beit).
• The eventVAE code is based on Phil Wang's code (https://github.com/lucidrains/DALLE-pytorch)