Humans can naturally and effectively find salient regions in complex scenes. Motivated by
this observation, attention mechanisms were introduced into computer vision with the aim of …

Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …

While originally designed for natural language processing tasks, the self-attention
mechanism has recently taken various computer vision areas by storm. However, the 2D …

We present techniques for scaling Swin Transformer [??] up to 3 billion parameters and
making it capable of training with images of up to 1,536 x1, 536 resolution. By scaling up …

Object detection is a well-known task in the field of computer vision, especially the small
target detection problem that has aroused great academic attention. In order to improve the …

We propose 3DETR, an end-to-end Transformer based object detection model for 3D point
clouds. Compared to existing detection methods that employ a number of 3D-specific …

The vision community is witnessing a modeling shift from CNNs to Transformers, where pure
Transformer architectures have attained top accuracy on the major video recognition …

Transformer, an attention-based encoder–decoder model, has already revolutionized the
field of natural language processing (NLP). Inspired by such significant achievements, some …

In the past few years, convolutional neural networks (CNNs) have achieved milestones in
medical image analysis. In particular, deep neural networks based on U-shaped architecture …

Previous pseudo-label approaches for semi-supervised object detection typically follow a
multi-stage schema, with the first stage to train an initial detector on a few labeled data …