Review the state-of-the-art technologies of semantic segmentation based on deep learning
The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …
information and predict the semantic category of each pixel from a given label set. With the …
Recent progress in semantic image segmentation
Semantic image segmentation, which becomes one of the key applications in image
processing and computer vision domain, has been used in multiple domains such as …
processing and computer vision domain, has been used in multiple domains such as …
Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation
G Papandreou, LC Chen… - Proceedings of the …, 2015 - openaccess.thecvf.com
Deep convolutional neural networks (DCNNs) trained on a large number of images with
strong pixel-level annotations have recently significantly pushed the state-of-art in semantic …
strong pixel-level annotations have recently significantly pushed the state-of-art in semantic …
Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
Recent leading approaches to semantic segmentation rely on deep convolutional networks
trained with human-annotated, pixel-level segmentation masks. Such pixel-accurate …
trained with human-annotated, pixel-level segmentation masks. Such pixel-accurate …
HCP: A flexible CNN framework for multi-label image classification
Convolutional Neural Network (CNN) has demonstrated promising performance in single-
label image classification tasks. However, how CNN best copes with multi-label images still …
label image classification tasks. However, how CNN best copes with multi-label images still …
Stc: A simple to complex framework for weakly-supervised semantic segmentation
Recently, significant improvement has been made on semantic object segmentation due to
the development of deep convolutional neural networks (DCNNs). Training such a DCNN …
the development of deep convolutional neural networks (DCNNs). Training such a DCNN …
Deepcut: Object segmentation from bounding box annotations using convolutional neural networks
In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations
given an image dataset labelled weak annotations, in our case bounding boxes. It extends …
given an image dataset labelled weak annotations, in our case bounding boxes. It extends …
Methods and datasets on semantic segmentation: A review
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 …
semantic label (eg car, people, and road) to each pixel of an image. It is an essential data …
Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation
Image segmentation refers to the process to divide an image into meaningful non-
overlapping regions according to human perception, which has become a classic topic since …
overlapping regions according to human perception, which has become a classic topic since …
Two-phase learning for weakly supervised object localization
Weakly supervised semantic segmentation and localization have a problem of focusing only
on the most important parts of an image since they use only image-level annotations. In this …
on the most important parts of an image since they use only image-level annotations. In this …