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

Some of the biological knowledge obtained from fundamental research will be implemented
in applied plant breeding. To bridge basic research and breeding practice, machine learning …

Self-supervised learning (SSL) is a scalable way to learn general visual representations
since it learns without labels. However, large-scale unlabeled datasets in the wild often have …

We introduce The House Of inteRactions (THOR), a framework for visual AI research,
available at http://ai2thor. allenai. org. AI2-THOR consists of near photo-realistic 3D indoor …

Graph Contrastive Learning (GCL) establishes a new paradigm for learning graph
representations without human annotations. Although remarkable progress has been …

Recent self-supervised representation learning techniques have largely closed the gap
between supervised and unsupervised learning on ImageNet classification. While the …

Abstract Vision Transformers (ViTs) have gained significant popularity in recent years and
have proliferated into many applications. However, their behavior under different learning …

Empowered by large datasets, eg, ImageNet and MS COCO, unsupervised learning on
large-scale data has enabled significant advances for classification tasks. However, whether …

A vital and rapidly growing application, remote sensing offers vast yet sparsely labeled,
spatially aligned multimodal data; this makes self-supervised learning algorithms invaluable …

Self-supervised models are increasingly prevalent in machine learning (ML) since they
reduce the need for expensively labeled data. Because of their versatility in downstream …