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 …

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, 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 …

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 …

Two-stage and query-based instance segmentation methods have achieved remarkable
results. However, their segmented masks are still very coarse. In this paper, we present …