Abstract The recent Segment Anything Model (SAM) represents a big leap in scaling up
segmentation models, allowing for powerful zero-shot capabilities and flexible prompting …

Neural network based computer vision systems are typically built on a backbone, a
pretrained or randomly initialized feature extractor. Several years ago, the default option was …

The revolutionary capabilities of large language models (LLMs) have paved the way for
multimodal large language models (MLLMs) and fostered diverse applications across …

Reconstructing human vision from brain activities has been an appealing task that helps to
understand our cognitive process. Even though recent research has seen great success in …

Abstract The recent Segment Anything Model (SAM) is a significant advancement in natural
image segmentation, exhibiting potent zero-shot performance suitable for various …

Transformer models are revolutionizing machine learning, but their inner workings remain
mysterious. In this work, we present a new visualization technique designed to help …

In recent decades, wildfires have caused tremendous property losses, fatalities, and
extensive damage to forest ecosystems. Inspired by the abundance of publicly available …

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

Background Deep learning (DL) models can potentially improve prognostication of rectal
cancer but have not been systematically assessed. Purpose To develop and validate an MRI …

Transformers have had a significant impact on natural language processing and have
recently demonstrated their potential in computer vision. They have shown promising results …