In this work we address various segmentation tasks each traditionally tackled by distinct or
partially unified models. We propose OMG-Seg One Model that is Good enough to efficiently …

Masked Autoencoders (MAE) have been popular paradigms for large-scale vision
representation pre-training. However, MAE solely reconstructs the low-level RGB signals …

Self-supervised landmark estimation is a challenging task that demands the formation of
locally distinct feature representations to identify sparse facial landmarks in the absence of …

This paper introduces a self-supervised learning framework designed for pre-training neural
networks tailored to dense prediction tasks using event camera data. Our approach utilizes …

Inspired by the fact that human brains can emphasize discriminative parts of the input and
suppress irrelevant ones, substantial local mechanisms have been designed to boost the …

Masked Image Modeling (MIM) is a powerful self-supervised strategy for visual pre-training
without the use of labels. MIM applies random crops to input images, processes them with …

Contrastive learning demonstrates great promise for representation learning. Data
augmentations play a critical role in contrastive learning by providing informative views of …

We present a novel frequency-based Self-Supervised Learning (SSL) approach that
significantly enhances its efficacy for pre-training. Prior work in this direction masks out pre …

Transformer-based models have demonstrated substantial potential in medical image
segmentation tasks due to their exceptional ability to capture long-range dependencies. To …

Recent progress in self-supervised representation learning has shown that self-supervised
pre-training can leverage unlabeled data to learn generalizable representations that benefit …