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

Image segmentation is often ambiguous at the level of individual image patches and
requires contextual information to reach label consensus. In this paper we introduce …

Current semantic segmentation methods mainly focus on modeling the context of the global
image to obtain high-quality segmentation results. However, they ignore the role of local …

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

Semantic segmentation is a challenging problem due to difficulties in modeling context in
complex scenes and class confusions along boundaries. Most literature either focuses on …

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 …

Full-image dependencies provide useful contextual information to benefit visual
understanding problems. In this work, we propose a Criss-Cross Network (CCNet) for …

State-of-the-art models for semantic segmentation are based on adaptations of
convolutional networks that had originally been designed for image classification. However …

Recent studies witnessed that context features can significantly improve the performance of
deep semantic segmentation networks. Current context based segmentation methods differ …

Recent advances in semantic image segmentation have mostly been achieved by training
deep convolutional neural networks (CNNs). We show how to improve semantic …