Humans can naturally and effectively find salient regions in complex scenes. Motivated by
this observation, attention mechanisms were introduced into computer vision with the aim of …

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 …

Prevalent semantic segmentation solutions, despite their different network designs (FCN
based or attention based) and mask decoding strategies (parametric softmax based or pixel …

We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation and
propose the SegViT. Previous ViT-based segmentation networks usually learn a pixel-level …

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

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 …

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 …

Metal surface defect segmentation can play an important role in dealing with the issue of
quality control during the production and manufacturing stages. There are still two major …

In this article, we propose a Dual Relation-aware Attention Network (DRANet) to handle the
task of scene segmentation. How to efficiently exploit context is essential for pixel-level …