Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

The semantic image segmentation task consists of classifying each pixel of an image into an
instance, where each instance corresponds to a class. This task is a part of the concept of …

To design fast neural networks, many works have been focusing on reducing the number of
floating-point operations (FLOPs). We observe that such reduction in FLOPs, however, does …

Attention mechanism, one of the most extensively utilized components in computer vision,
can assist neural networks in emphasizing significant elements and suppressing irrelevant …

Cancer is a fatal disease caused by a combination of genetic diseases and a variety of
biochemical abnormalities. Lung and colon cancer have emerged as two of the leading …

Transformers have attracted increasing interests in computer vision, but they still fall behind
state-of-the-art convolutional networks. In this work, we show that while Transformers tend to …

Abstract Convolutional Neural Networks (CNNs) are the go-to model for computer vision.
Recently, attention-based networks, such as the Vision Transformer, have also become …

Very recently, a variety of vision transformer architectures for dense prediction tasks have
been proposed and they show that the design of spatial attention is critical to their success in …

This paper introduces EfficientNetV2, a new family of convolutional networks that have faster
training speed and better parameter efficiency than previous models. To develop these …

Self-attention has the promise of improving computer vision systems due to parameter-
independent scaling of receptive fields and content-dependent interactions, in contrast to …