Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

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

As acquiring pixel-wise annotations of real-world images for semantic segmentation is a
costly process, a model can instead be trained with more accessible synthetic data and …

Convolutional neural networks (CNNs) have so far been the de-facto model for visual data.
Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or …

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 …

Transformer emerges as a powerful tool for visual recognition. In addition to demonstrating
competitive performance on a broad range of visual benchmarks, recent works also argue …

Vision transformers (ViT) have demonstrated impressive performance across numerous
machine vision tasks. These models are based on multi-head self-attention mechanisms that …

Vision transformers have shown great potential in various computer vision tasks owing to
their strong capability to model long-range dependency using the self-attention mechanism …

Most polyp segmentation methods use CNNs as their backbone, leading to two key issues
when exchanging information between the encoder and decoder: 1) taking into account the …

Vision Transformers convert images to sequences by slicing them into patches. The size of
these patches controls a speed/accuracy tradeoff, with smaller patches leading to higher …