Self-supervised learning for medical image classification: a systematic review and implementation guidelines
Advancements in deep learning and computer vision provide promising solutions for
medical image analysis, potentially improving healthcare and patient outcomes. However …
medical image analysis, potentially improving healthcare and patient outcomes. However …
Self-supervised remote sensing feature learning: Learning paradigms, challenges, and future works
Deep learning has achieved great success in learning features from massive remote
sensing images (RSIs). To better understand the connection between three feature learning …
sensing images (RSIs). To better understand the connection between three feature learning …
Semmae: Semantic-guided masking for learning masked autoencoders
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 language modeling. However, unlike words in NLP, the lack of semantic …
Hard patches mining for masked image modeling
Masked image modeling (MIM) has attracted much research attention due to its promising
potential for learning scalable visual representations. In typical approaches, models usually …
potential for learning scalable visual representations. In typical approaches, models usually …
Mixed autoencoder for self-supervised visual representation learning
Masked Autoencoder (MAE) has demonstrated superior performance on various vision tasks
via randomly masking image patches and reconstruction. However, effective data …
via randomly masking image patches and reconstruction. However, effective data …
Mart: Masked affective representation learning via masked temporal distribution distillation
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 …
works leverage the power of large-scale image datasets for transferring while failing to …
Understanding masked image modeling via learning occlusion invariant feature
X Kong, X Zhang - … of the IEEE/CVF Conference on …, 2023 - openaccess.thecvf.com
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 …
visual recognition. However, as a reconstruction-based framework, it is still an open …
Salience-based adaptive masking: revisiting token dynamics for enhanced pre-training
In this paper, we introduce Saliency-Based Adaptive Masking (SBAM), a novel and cost-
effective approach that significantly enhances the pre-training performance of Masked …
effective approach that significantly enhances the pre-training performance of Masked …
Anatomical invariance modeling and semantic alignment for self-supervised learning in 3d medical image analysis
Self-supervised learning (SSL) has recently achieved promising performance for 3D medical
image analysis tasks. Most current methods follow existing SSL paradigm originally …
image analysis tasks. Most current methods follow existing SSL paradigm originally …
Embedding global contrastive and local location in self-supervised learning
Self-supervised representation learning (SSL) typically suffers from inadequate data
utilization and feature-specificity due to the suboptimal sampling strategy and the …
utilization and feature-specificity due to the suboptimal sampling strategy and the …