Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

Image segmentation is a crucial task in computer vision, which divides a digital image into
multiple segments and objects. In agriculture, image segmentation is extensively used for …

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 …

Most recent semantic segmentation methods adopt a fully-convolutional network (FCN) with
an encoder-decoder architecture. The encoder progressively reduces the spatial resolution …

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 …

Current semantic segmentation methods focus only on mining" local" context, ie,
dependencies between pixels within individual images, by context-aggregation modules …

Spatial pooling has been proven highly effective to capture long-range contextual
information for pixel-wise prediction tasks, such as scene parsing. In this paper, beyond …

This paper introduces SuperGlue, a neural network that matches two sets of local features
by jointly finding correspondences and rejecting non-matchable points. Assignments are …

In this paper, we study the context aggregation problem in semantic segmentation.
Motivated by that the label of a pixel is the category of the object that the pixel belongs to, we …

This paper focuses on the unsupervised domain adaptation of transferring the knowledge
from the source domain to the target domain in the context of semantic segmentation …