Image instance segmentation involves labeling pixels of images with classes and instances,
which is one of the pivotal technologies in many domains, such as natural scenes …

Transformer, an attention-based encoder–decoder model, has already revolutionized the
field of natural language processing (NLP). Inspired by such significant achievements, some …

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 …

Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

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 …

In this paper, we propose a conceptually novel, efficient, and fully convolutional framework
for real-time instance segmentation. Previously, most instance segmentation methods …

Recently, the vision transformer has achieved great success by pushing the state-of-the-art
of various vision tasks. One of the most challenging problems in the vision transformer is that …

We present CLUSTSEG, a general, transformer-based framework that tackles different
image segmentation tasks (ie, superpixel, semantic, instance, and panoptic) through a …

End-to-end scene text spotting has attracted great attention in recent years due to the
success of excavating the intrinsic synergy of the scene text detection and recognition …

摘要Transformer 所具备的长距离建模能力和并行计算能力使其在自然语言处理领域取得了
巨大成功并逐步拓展至计算机视觉等领域. 本文以分类任务为切入, 介绍了典型视觉Transformer …