With the urgent demand for generalized deep models, many pre-trained big models are
proposed, such as bidirectional encoder representations (BERT), vision transformer (ViT) …

Cooperative computing is promising to enhance the performance and safety of autonomous
vehicles benefiting from the increase in the amount, diversity as well as scope of data …

Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

Currently, the automatic segmentation of breast tumors based on breast ultrasound (BUS)
images is still a challenging task. Most lesion segmentation methods are implemented …

Transformer models have shown great success handling long-range interactions, making
them a promising tool for modeling video. However, they lack inductive biases and scale …

Transformer 所具备的长距离建模能力和并行计算能力使其在自然语言处理领域取得了巨大
成功并逐步拓展至计算机视觉等领域. 本文以分类任务为切入, 介绍了典型视觉Transformer …

Most semi-supervised skeleton-based action recognition approaches aim to learn the
skeleton action representations only at the joint level, but neglect the crucial motion …

Semantic segmentation has a broad range of applications in a variety of domains including
land coverage analysis, autonomous driving, and medical image analysis. Convolutional …

The remarkable success of transformers in the field of natural language processing has
sparked the interest of the speech-processing community, leading to an exploration of their …

Transformers have demonstrated impressive expressiveness and transfer capability in
computer vision fields. Dense prediction is a fundamental problem in computer vision that is …