Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …

Astounding results from Transformer models on natural language tasks have intrigued the
vision community to study their application to computer vision problems. Among their salient …

Abstract We introduce the Segment Anything (SA) project: a new task, model, and dataset for
image segmentation. Using our efficient model in a data collection loop, we built the largest …

Today's deep learning methods focus on how to design the objective functions to make the
prediction as close as possible to the target. Meanwhile, an appropriate neural network …

As the core building block of vision transformers, attention is a powerful tool to capture long-
range dependency. However, such power comes at a cost: it incurs a huge computation …

Large language models (LLMs) have notably accelerated progress towards artificial general
intelligence (AGI), with their impressive zero-shot capacity for user-tailored tasks, endowing …

Recently the state space models (SSMs) with efficient hardware-aware designs, ie, the
Mamba deep learning model, have shown great potential for long sequence modeling …

Compared to the great progress of large-scale vision transformers (ViTs) in recent years,
large-scale models based on convolutional neural networks (CNNs) are still in an early …

Vision transformers have shown great success due to their high model capabilities.
However, their remarkable performance is accompanied by heavy computation costs, which …

Recent research on remote sensing object detection has largely focused on improving the
representation of oriented bounding boxes but has overlooked the unique prior knowledge …