The semantic image segmentation task consists of classifying each pixel of an image into an
instance, where each instance corresponds to a class. This task is a part of the concept of …

Scene graph is a structured representation of a scene that can clearly express the objects,
attributes, and relationships between objects in the scene. As computer vision technology …

Self-attention mechanism has been widely used for various tasks. It is designed to compute
the representation of each position by a weighted sum of the features at all positions. Thus, it …

Contextual information has been shown to be powerful for semantic segmentation. This work
proposes a novel Context-based Tandem Network (CTNet) by interactively exploring the …

Recent work has made significant progress in improving spatial resolution for pixelwise
labeling with Fully Convolutional Network (FCN) framework by employing Dilated/Atrous …

Spatial pyramid pooling module or encode-decoder structure are used in deep neural
networks for semantic segmentation task. The former networks are able to encode multi …

The recent integration of attention mechanisms into segmentation networks improves their
representational capabilities through a great emphasis on more informative features …

Recent work has achieved great success in utilizing global contextual information for
semantic segmentation, including increasing the receptive field and aggregating pyramid …

Recent semantic segmentation methods exploit encoder-decoder architectures to produce
the desired pixel-wise segmentation prediction. The last layer of the decoders is typically a …

Semantic segmentation, also called scene labeling, refers to the process of assigning a
semantic label (eg car, people, and road) to each pixel of an image. It is an essential data …