  This paper proposes a pre-trained neural network for handling event camera
data. Our model is a self-supervised learning framework, and uses paired event
camera data and natural RGB images for training.
  Our method contains three modules connected in a sequence: i) a family of
event data augmentations, generating meaningful event images for
self-supervised training; ii) a conditional masking strategy to sample
informative event patches from event images, encouraging our model to capture
the spatial layout of a scene and accelerating training; iii) a contrastive
learning approach, enforcing the similarity of embeddings between matching
event images, and between paired event and RGB images. An embedding projection
loss is proposed to avoid the model collapse when enforcing the event image
embedding similarities. A probability distribution alignment loss is proposed
to encourage the event image to be consistent with its paired RGB image in the
feature space.
  Transfer learning performance on downstream tasks shows the superiority of
our method over state-of-the-art methods. For example, we achieve top-1
accuracy at 64.83% on the N-ImageNet dataset.
