Query: weakly-supervised semantic segmentation

title: A survey of semi and weakly supervised semantic segmentation of images
abstract: Image semantic segmentation is one of the most important tasks in the field of computer vision, and it has made great progress in many applications. Many fully supervised deep learning models are designed to implement complex semantic segmentation tasks and the experimental results are remarkable. However, the acquisition of pixel level labels in fully supervised learning is time consuming and laborious, semi supervised and weakly supervised learning is gradually replacing fully supervised learning, thus achieving good results at a lower cost. Based on the commonly used models such as convolutional neural networks, fully convolutional networks, generative adversarial networks, this paper focuses on the core methods and reviews the semi and weakly supervised semantic segmentation models in recent years. In the following chapters, existing evaluations and data sets are summarized in details and the experimental results are analyzed according to the data set. The last part of the paper is an objective summary. In addition, it points out the possible direction of research and inspiring suggestions for future work.

title: Survey of weakly supervised semantic segmentation methods
abstract: Weakly supervised image semantic segmentation is quite popular in computer vision and machine learning today. The existing supervised semantic segmentation approaches require lots of pixel based annotation for training, which are labor and time consuming to obtain. By comparison, weakly supervised methods don't need the pixel level label, and they can do the semantic segmentation work by only using the image level label. This paper provides a review on weakly supervised semantic segmentation methods. First, the existing methods are described. Then, the main datasets and evaluating indicator are exposed. Subsequently, the results are discussed. Finally, the conclusions are given.

title: Weakly Supervised Semantic Segmentation Based on Deep Learning
abstract: Weakly supervised semantic segmentation has important significance and research value in computer vision. Although the traditional semantic segmentation based on fully supervised learning has achieved great success, the training network needs to rely on a large number of pixel level labeled data. It is difficult and time consuming to obtain such high quality annotation. In order to alleviate the huge burden of pixel level labels, many weakly supervised semantic segmentation methods have emerged in recent years. This paper reviews the research progress of weakly supervised semantic segmentation. Firstly, we introduce the existing weakly supervised methods, highlighting their respective contributions and positive impact in this field. Then, datasets and metrics are described. Moreover, the results are analyzed and discussed. Finally, this work summarizes the difficulties of weakly supervised semantic segmentation and further explores the development direction of weakly supervised semantic segmentation in the future.

title: Weakly Supervised Learning with Deep Convolutional Neural Networks for Semantic Segmentation: Understanding Semantic Layout of Images with Minimum Human Supervision
abstract: Semantic segmentation is a popular visual recognition task whose goal is to estimate pixel level object class labels in images. This problem has been recently handled by deep convolutional neural networks (DCNNs) and the state of theart techniques achieve impressive records on public benchmark data sets. However, learning DCNNs demand a large number of annotated training data while segmentation annotations in existing data sets are significantly limited in terms of both quantity and diversity due to the heavy annotation cost. Weakly supervised approaches tackle this issue by leveraging weak annotations such as image level labels and bounding boxes, which are either readily available in existing large scale data sets for image classification and object detection or easily obtained thanks to their low annotation costs. The main challenge in weakly supervised semantic segmentation then is the incomplete annotations that miss accurate object boundary information required to learn segmentation. This article provides a comprehensive overview of weakly supervised approaches for semantic segmentation. Specifically, we describe how the approaches overcome the limitations and discuss research directions worthy of investigation to improve performance.

title: HistoSegNet: Semantic Segmentation of Histological Tissue Type in Whole Slide Images
abstract: In digital pathology, tissue slides are scanned into Whole Slide Images (WSI) and pathologists first screen for diagnostically relevant Regions of Interest (ROIs) before reviewing them. Screening for ROIs is a tedious and time consuming visual recognition task which can be exhausting. The cognitive workload could be reduced by developing a visual aid to narrow down the visual search area by highlighting (or segmenting) regions of diagnostic relevance, enabling pathologists to spend more time diagnosing relevant ROIs. In this paper, we propose HistoSegNet, a method for semantic segmentation of histological tissue type (HTT) Using the HTT annotated Atlas of Digital Pathology (ADP) database, we train a Convolutional Neural Network on the patch annotations, infer Gradient Weighted Class Activation Maps, average overlapping predictions, and post process the segmentation with a fully connected Conditional Random Field. Our method out performs more complicated weakly supervised semantic segmentation methods and can generalize to other datasets without retraining.

title: Methods and datasets on semantic segmentation: A review
abstract: Semantic segmentation, also called scene labeling, refers to the process of assigning a semantic label (e.g. car, people, and road) to each pixel of an image. It is an essential data processing step for robots and other unmanned systems to understand the surrounding scene. Despite decades of efforts, semantic segmentation is still a very challenging task due to large variations in natural scenes. In this paper, we provide a systematic review of recent advances in this field. In particular, three categories of methods are reviewed and compared, including those based on hand engineered features, learned features and weakly supervised learning. In addition, we describe a number of popular datasets aiming for facilitating the development of new segmentation algorithms. In order to demonstrate the advantages and disadvantages of different semantic segmentation models, we conduct a series of comparisons between them. Deep discussions about the comparisons are also provided. Finally, this review is concluded by discussing future directions and challenges in this important field of research.

title: Deep semantic segmentation of natural and medical images: a review
abstract: The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class. This task is a part of the concept of scene understanding or better explaining the global context of an image. In the medical image analysis domain, image segmentation can be used for image guided interventions, radiotherapy, or improved radiological diagnostics. In this review, we categorize the leading deep learning based medical and non medical image segmentation solutions into six main groups of deep architectural, data synthesis based, loss function based, sequenced models, weakly supervised, and multi task methods and provide a comprehensive review of the contributions in each of these groups. Further, for each group, we analyze each variant of these groups and discuss the limitations of the current approaches and present potential future research directions for semantic image segmentation.

title: Weakly supervised land classification for coastal zone based on deep convolutional neural networks by incorporating dual polarimetric characteristics into training dataset
abstract: In this work we explore the performance of DCNNs on semantic segmentation using spaceborne polarimetric synthetic aperture radar (PolSAR) datasets. The semantic segmentation task using PolSAR data can be categorized as weakly supervised learning when the characteristics of SAR data and data annotating procedures are factored in. Datasets are initially analyzed for selecting feasible pre training images. Then the differences between spaceborne and airborne datasets are examined in terms of spatial resolution and viewing geometry. In this study we used two dual polarimetric images acquired by TerraSAR X DLR. A novel method to produce training dataset with more supervised information is developed. Specifically, a series of typical classified images as well as intensity images serve as training datasets. A field survey is conducted for an area of about 20 square kilometers to obtain a ground truth dataset used for accuracy evaluation. Several transfer learning strategies are made for aforementioned training datasets which will be combined in a practicable order. Three DCNN models, including SegNet, U Net, and LinkNet, are implemented next.

title: A Brief Survey on Semantic Segmentation with Deep Learning
abstract: Semantic segmentation is a challenging task in computer vision. In recent years, the performance of semantic segmentation has been greatly improved by using deep learning techniques. A large number of novel methods have been proposed. This paper aims to provide a brief review of research efforts on deep learning based semantic segmentation methods. We categorize the related research according to its supervision level, i.e. fully supervised methods, weakly supervised methods and semi supervised methods. We also discuss the common challenges of the current research, and present several valuable growing research points in this field. This survey is expected to familiarize readers with the progress and challenges of semantic segmentation research in the deep learning era.

title: Automated Extraction of Human Settlement Patterns From Historical Topographic Map Series Using Weakly Supervised Convolutional Neural Networks
abstract: Information extraction from historical maps represents a persistent challenge due to inferior graphical quality and the large data volume of digital map archives, which can hold thousands of digitized map sheets. Traditional map processing techniques typically rely on manually collected templates of the symbol of interest, and thus are not suitable for large scale information extraction. In order to digitally preserve such large amounts of valuable retrospective geographic information, high levels of automation are required. Herein, we propose an automated machine learning based framework to extract human settlement symbols, such as buildings and urban areas from historical topographic maps in the absence of training data, employing contemporary geospatial data as ancillary data to guide the collection of training samples. These samples are then used to train a convolutional neural network for semantic image segmentation, allowing for the extraction of human settlement patterns in an analysis ready geospatial vector data format. We test our method on United States Geological Survey historical topographic maps published between 1893 and 1954. The results are promising, indicating high degrees of completeness in the extracted settlement features (i.e. recall of up to 0.96, F measure of up to 0.79) and will guide the next steps to provide a fully automated operational approach for large scale geographic feature extraction from a variety of historical map series. Moreover, the proposed framework provides a robust approach for the recognition of objects which are small in size, generalizable to many kinds of visual documents.