Efficient Image Pre-Training with Siamese Cropped Masked Autoencoders
Alexandre Eymaël Renaud Vandeghen Anthony Cioppa Silvio Giancola Bernard Ghanem Marc Van Droogenbroeck
Self-supervised pre-training of image encoders is omnipresent in the
literature, particularly following the introduction of Masked autoencoders
(MAE). Current efforts attempt to learn object-centric representations from
motion in videos. In particular, SiamMAE recently introduced a Siamese network,
training a shared-weight encoder from two frames of a video with a high
asymmetric masking ratio (95%). In this work, we propose CropMAE, an
alternative approach to the Siamese pre-training introduced by SiamMAE. Our
method specifically differs by exclusively considering pairs of cropped images
sourced from the same image but cropped differently, deviating from the
conventional pairs of frames extracted from a video. CropMAE therefore
alleviates the need for video datasets, while maintaining competitive
performances and drastically reducing pre-training time. Furthermore, we
demonstrate that CropMAE learns similar object-centric representations without
explicit motion, showing that current self-supervised learning methods do not
learn objects from motion, but rather thanks to the Siamese architecture.
Finally, CropMAE achieves the highest masking ratio to date (98.5%), enabling
the reconstruction of images using only two visible patches. Our code is
available at https://github.com/alexandre-eymael/CropMAE.