The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

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 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 …

In this paper, we address the semantic segmentation task with a new context aggregation
scheme named object context, which focuses on enhancing the role of object information …

Multi-scale inference is commonly used to improve the results of semantic segmentation.
Multiple images scales are passed through a network and then the results are combined …

Automatically detecting/segmenting object (s) that blend in with their surroundings is difficult
for current models. A major challenge is that the intrinsic similarities between such …

Semantic segmentation of remote sensing images plays an important role in a wide range of
applications, including land resource management, biosphere monitoring, and urban …

Existing semantic segmentation approaches either aim to improve the object's inner
consistency by modeling the global context, or refine objects detail along their boundaries …

In this paper, we focus on designing effective method for fast and accurate scene parsing. A
common practice to improve the performance is to attain high resolution feature maps with …