Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

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 …

Recently many deep models have been proposed for multivariate time series (MTS)
forecasting. In particular, Transformer-based models have shown great potential because …

Network architecture plays a key role in the deep learning-based computer vision system.
The widely-used convolutional neural network and transformer treat the image as a grid or …

In this paper, we develop position embedding transformation (PETR) for multi-view 3D
object detection. PETR encodes the position information of 3D coordinates into image …

Abstract 3D point cloud segmentation has made tremendous progress in recent years. Most
current methods focus on aggregating local features, but fail to directly model long-range …

Transformers have shown great potential in computer vision tasks. A common belief is their
attention-based token mixer module contributes most to their competence. However, recent …

We present a framework for video modeling based on denoising diffusion probabilistic
models that produces long-duration video completions in a variety of realistic environments …

In this work, we introduce Dual Attention Vision Transformers (DaViT), a simple yet effective
vision transformer architecture that is able to capture global context while maintaining …

Vision transformer (ViT) recently has drawn great attention in computer vision due to its
remarkable model capability. However, most prevailing ViT models suffer from huge number …