Semantic Segmentation using Generative Adversarial Networks with a Feature Reconstruction Loss

2021 ◽  
Author(s):  
Gaurav Sohaliya ◽  
Kapil Sharma
Keyword(s):  
Semantic Segmentation ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Feature Reconstruction

Semantic Segmentation of High Resolution Satellite Imagery using Generative Adversarial Networks with Progressive Growing

2021 ◽  
Vol 12 (5) ◽  
pp. 439-448
Author(s):  
Edward Collier ◽  
Supratik Mukhopadhyay ◽  
Kate Duffy ◽  
Sangram Ganguly ◽  
Geri Madanguit ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Semantic Segmentation ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
High Resolution Satellite Imagery

scholarly journals Unsupervised Domain Adaptation Using Generative Adversarial Networks for Semantic Segmentation of Aerial Images

2019 ◽  
Vol 11 (11) ◽  
pp. 1369 ◽  
Author(s):  
Bilel Benjdira ◽  
Yakoub Bazi ◽  
Anis Koubaa ◽  
Kais Ouni
Keyword(s):  
Urban Areas ◽  
Traffic Management ◽  
Domain Adaptation ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Generative Adversarial Networks ◽  
Target Domain ◽  
Adversarial Networks ◽  
Segmentation Accuracy ◽  
New City

Segmenting aerial images is of great potential in surveillance and scene understanding of urban areas. It provides a mean for automatic reporting of the different events that happen in inhabited areas. This remarkably promotes public safety and traffic management applications. After the wide adoption of convolutional neural networks methods, the accuracy of semantic segmentation algorithms could easily surpass 80% if a robust dataset is provided. Despite this success, the deployment of a pretrained segmentation model to survey a new city that is not included in the training set significantly decreases accuracy. This is due to the domain shift between the source dataset on which the model is trained and the new target domain of the new city images. In this paper, we address this issue and consider the challenge of domain adaptation in semantic segmentation of aerial images. We designed an algorithm that reduces the domain shift impact using generative adversarial networks (GANs). In the experiments, we tested the proposed methodology on the International Society for Photogrammetry and Remote Sensing (ISPRS) semantic segmentation dataset and found that our method improves overall accuracy from 35% to 52% when passing from the Potsdam domain (considered as source domain) to the Vaihingen domain (considered as target domain). In addition, the method allows efficiently recovering the inverted classes due to sensor variation. In particular, it improves the average segmentation accuracy of the inverted classes due to sensor variation from 14% to 61%.

scholarly journals Learning Segmentation Masks with the Independence Prior

2019 ◽  
Vol 33 ◽  
pp. 3429-3436 ◽  
Author(s):  
Songmin Dai ◽  
Xiaoqiang Li ◽  
Lu Wang ◽  
Pin Wu ◽  
Weiqin Tong ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Generative Adversarial Networks ◽  
Composite Image ◽  
Specific Instance ◽  
Adversarial Networks ◽  
Segmentation Methods ◽  
Multiple Category ◽  
Complete Category ◽  
Weakly Supervised ◽  
Instance Segmentation

An instance with a bad mask might make a composite image that uses it look fake. This encourages us to learn segmentation by generating realistic composite images. To achieve this, we propose a novel framework that exploits a new proposed prior called the independence prior based on Generative Adversarial Networks (GANs). The generator produces an image with multiple category-specific instance providers, a layout module and a composition module. Firstly, each provider independently outputs a category-specific instance image with a soft mask. Then the provided instances’ poses are corrected by the layout module. Lastly, the composition module combines these instances into a final image. Training with adversarial loss and penalty for mask area, each provider learns a mask that is as small as possible but enough to cover a complete category-specific instance. Weakly supervised semantic segmentation methods widely use grouping cues modeling the association between image parts, which are either artificially designed or learned with costly segmentation labels or only modeled on local pairs. Unlike them, our method automatically models the dependence between any parts and learns instance segmentation. We apply our framework in two cases: (1) Foreground segmentation on category-specific images with box-level annotation. (2) Unsupervised learning of instance appearances and masks with only one image of homogeneous object cluster (HOC). We get appealing results in both tasks, which shows the independence prior is useful for instance segmentation and it is possible to unsupervisedly learn instance masks with only one image.

scholarly journals A Multi-Stage GAN for Multi-Organ Chest X-ray Image Generation and Segmentation

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2896
Author(s):  
Giorgio Ciano ◽  
Paolo Andreini ◽  
Tommaso Mazzierli ◽  
Monica Bianchini ◽  
Franco Scarselli
Keyword(s):  
Data Augmentation ◽  
Semantic Segmentation ◽  
Generative Adversarial Networks ◽  
Radiographic Images ◽  
X Ray ◽  
Adversarial Networks ◽  
Multi Stage ◽  
Segmentation Methods ◽  
Chest X Ray ◽  
Time Required

Multi-organ segmentation of X-ray images is of fundamental importance for computer aided diagnosis systems. However, the most advanced semantic segmentation methods rely on deep learning and require a huge amount of labeled images, which are rarely available due to both the high cost of human resources and the time required for labeling. In this paper, we present a novel multi-stage generation algorithm based on Generative Adversarial Networks (GANs) that can produce synthetic images along with their semantic labels and can be used for data augmentation. The main feature of the method is that, unlike other approaches, generation occurs in several stages, which simplifies the procedure and allows it to be used on very small datasets. The method was evaluated on the segmentation of chest radiographic images, showing promising results. The multi-stage approach achieves state-of-the-art and, when very few images are used to train the GANs, outperforms the corresponding single-stage approach.

scholarly journals Scene Understanding Based on High-Order Potentials and Generative Adversarial Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Xiaoli Zhao ◽  
Guozhong Wang ◽  
Jiaqi Zhang ◽  
Xiang Zhang
Keyword(s):  
Scene Understanding ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
High Order ◽  
Generative Adversarial Networks ◽  
Class Label ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Segmented Image ◽  
Segmentation Framework

Scene understanding is to predict a class label at each pixel of an image. In this study, we propose a semantic segmentation framework based on classic generative adversarial nets (GAN) to train a fully convolutional semantic segmentation model along with an adversarial network. To improve the consistency of the segmented image, the high-order potentials, instead of unary or pairwise potentials, are adopted. We realize the high-order potentials by substituting adversarial network for CRF model, which can continuously improve the consistency and details of the segmented semantic image until it cannot discriminate the segmented result from the ground truth. A number of experiments are conducted on PASCAL VOC 2012 and Cityscapes datasets, and the quantitative and qualitative assessments have shown the effectiveness of our proposed approach.

scholarly journals Multi-Source Fusion Image Semantic Segmentation Model of Generative Adversarial Networks Based on FCN

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 101985-101993
Author(s):  
Liang Zhao ◽  
Ying Wang ◽  
Zhongxing Duan ◽  
Dengfeng Chen ◽  
Shipeng Liu
Keyword(s):  
Semantic Segmentation ◽  
Generative Adversarial Networks ◽  
Fusion Image ◽  
Adversarial Networks

scholarly journals Object-Level Remote Sensing Image Augmentation Using U-Net-Based Generative Adversarial Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jian Huang ◽  
Shanhui Liu ◽  
Yutian Tang ◽  
Xiushan Zhang
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Remote Sensing Images ◽  
Adversarial Networks ◽  
Port Area ◽  
Object Level

With the continuous development of deep learning in computer vision, semantic segmentation technology is constantly employed for processing remote sensing images. For instance, it is a key technology to automatically mark important objects such as ships or port land from port area remote sensing images. However, the existing supervised semantic segmentation model based on deep learning requires a large number of training samples. Otherwise, it will not be able to correctly learn the characteristics of the target objects, which results in the poor performance or even failure of semantic segmentation task. Since the target objects such as ships may move from time to time, it is nontrivial to collect enough samples to achieve satisfactory segmentation performance. And this severely hinders the performance improvement of most of existing augmentation methods. To tackle this problem, in this paper, we propose an object-level remote sensing image augmentation approach based on leveraging the U-Net-based generative adversarial networks. Specifically, our proposed approach consists two components including the semantic tag image generator and the U-Net GAN-based translator. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a public dataset HRSC2016. State-of-the-art generative models, DCGAN, WGAN, and CycleGAN, are selected as baselines. According to the experimental results, our proposed approach significantly outperforms the baselines in terms of not only drawing the outlines of target objects but also capturing their meaningful details.

Sign in / Sign up

Export Citation Format

Share Document