Advancements in deep learning and computer vision provide promising solutions for
medical image analysis, potentially improving healthcare and patient outcomes. However …

Deep learning has achieved great success in learning features from massive remote
sensing images (RSIs). To better understand the connection between three feature learning …

Recently, significant progress has been made in masked image modeling to catch up to
masked language modeling. However, unlike words in NLP, the lack of semantic …

Masked image modeling (MIM) has attracted much research attention due to its promising
potential for learning scalable visual representations. In typical approaches, models usually …

Masked Autoencoder (MAE) has demonstrated superior performance on various vision tasks
via randomly masking image patches and reconstruction. However, effective data …

Limited training data is a long-standing problem for video emotion analysis (VEA). Existing
works leverage the power of large-scale image datasets for transferring while failing to …

Abstract Recently, Masked Image Modeling (MIM) achieves great success in self-supervised
visual recognition. However, as a reconstruction-based framework, it is still an open …

Self-supervised representation learning (SSL) typically suffers from inadequate data
utilization and feature-specificity due to the suboptimal sampling strategy and the …

Self-supervised learning (SSL) has recently achieved promising performance for 3D medical
image analysis tasks. Most current methods follow existing SSL paradigm originally …

The development of deep learning models in medical image analysis is majorly limited by
the lack of large-sized and well-annotated datasets. Unsupervised learning does not require …