Incorporating multi-scale features in fully convolutional neural networks (FCNs) has been a
key element to achieving state-of-the-art performance on semantic image segmentation …

Semantic segmentation aims to parse the scene structure of images by annotating the labels
to each pixel so that images can be segmented into different regions. While image structures …

State-of-the-art approaches for semantic image segmentation are built on Convolutional
Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a …

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

Semantic segmentation generates comprehensive understanding of scenes through
densely predicting the category for each pixel. High-level features from Deep Convolutional …

Modern semantic segmentation frameworks usually combine low-level and high-level
features from pre-trained backbone convolutional models to boost performance. In this …

In deep CNN based models for semantic segmentation, high accuracy relies on rich spatial
context (large receptive fields) and fine spatial details (high resolution), both of which incur …

Accurate semantic image segmentation requires the joint consideration of local appearance,
semantic information, and global scene context. In today's age of pre-trained deep networks …

We propose a novel deep layer cascade (LC) method to improve the accuracy and speed of
semantic segmentation. Unlike the conventional model cascade (MC) that is composed of …

In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-
view as well as control the resolution of feature responses computed by Deep Convolutional …