Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

Astounding results from Transformer models on natural language tasks have intrigued the
vision community to study their application to computer vision problems. Among their salient …

Vision transformers have shown great success due to their high model capabilities.
However, their remarkable performance is accompanied by heavy computation costs, which …

This study proposes a novel general image fusion framework based on cross-domain long-
range learning and Swin Transformer, termed as SwinFusion. On the one hand, an attention …

Abstract Vision Transformers (ViT) have shown rapid progress in computer vision tasks,
achieving promising results on various benchmarks. However, due to the massive number of …

With the success of Vision Transformers (ViTs) in computer vision tasks, recent arts try to
optimize the performance and complexity of ViTs to enable efficient deployment on mobile …

Transformers have achieved superior performances in many tasks in natural language
processing and computer vision, which also triggered great interest in the time series …

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 …

Relative position encoding (RPE) is important for transformer to capture sequence ordering
of input tokens. General efficacy has been proven in natural language processing. However …

Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …