We present pure-transformer based models for video classification, drawing upon the recent
success of such models in image classification. Our model extracts spatio-temporal tokens …

We design a family of image classification architectures that optimize the trade-off between
accuracy and efficiency in a high-speed regime. Our work exploits recent findings in …

Recently, neural networks purely based on attention were shown to address image
understanding tasks such as image classification. These high-performing vision …

Novel computer vision architectures monopolize the spotlight, but the impact of the model
architecture is often conflated with simultaneous changes to training methodology and …

We introduce the GANsformer, a novel and efficient type of transformer, and explore it for the
task of visual generative modeling. The network employs a bipartite structure that enables …

As the worldwide planting crop, rice feeds nearly half of the world's population. However, the
continuous spread of diseases is threatening rice production. It is of great practical value to …

We show how to augment any convolutional network with an attention-based global map to
achieve non-local reasoning. We replace the final average pooling by an attention-based …

Recently, Deepfake arises as a powerful tool to fool the existing real-world face detection
systems, which has received wide attention in both academia and society. Most existing …

Chest imaging plays an essential role in diagnosing and predicting patients with COVID-19
with evidence of worsening respiratory status. Many deep learning-based approaches for …

Learning representative, robust and discriminative information from images is essential for
effective person re-identification (Re-Id). In this paper, we propose a compound approach for …