scholarly journals H-Net: Neural Network for Cross-domain Image Patch Matching

2018 ◽  
Author(s):  
Weiquan Liu ◽  
Xuelun Shen ◽  
Cheng Wang ◽  
Zhihong Zhang ◽  
Chenglu Wen ◽  
...  
Keyword(s):  
3D Model ◽  
State Of The Art ◽  
Image Patch ◽  
Structural Shape ◽  
Cross Domain ◽  
Main Challenge ◽  
Image Dataset ◽  
Patch Matching ◽  
The Cross ◽  
Art Performance

Describing the same scene with different imaging style or rendering image from its 3D model gives us different domain images. Different domain images tend to have a gap and different local appearances, which raise the main challenge on the cross-domain image patch matching. In this paper, we propose to incorporate AutoEncoder into the Siamese network, named as H-Net, of which the structural shape resembles the letter H. The H-Net achieves state-of-the-art performance on the cross-domain image patch matching. Furthermore, we improved H-Net to H-Net++. The H-Net++ extracts invariant feature descriptors in cross-domain image patches and achieves state-of-the-art performance by feature retrieval in Euclidean space. As there is no benchmark dataset including cross-domain images, we made a cross-domain image dataset which consists of camera images, rendering images from UAV 3D model, and images generated by CycleGAN algorithm. Experiments show that the proposed H-Net and H-Net++ outperform the existing algorithms. Our code and cross-domain image dataset are available at https://github.com/Xylon-Sean/H-Net.

scholarly journals Blogs, Twitter Feeds, and Reddit Comments: Cross-domain Authorship Attribution

2016 ◽  
Vol 2016 (3) ◽  
pp. 155-171 ◽  
Author(s):  
Rebekah Overdorf ◽  
Rachel Greenstadt
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Security And Privacy ◽  
Authorship Attribution ◽  
The Internet ◽  
Privacy Concern ◽  
Writing Style ◽  
Improve Performance ◽  
Cross Domain ◽  
The Cross

AbstractStylometry is a form of authorship attribution that relies on the linguistic information to attribute documents of unknown authorship based on the writing styles of a suspect set of authors. This paper focuses on the cross-domain subproblem where the known and suspect documents differ in the setting in which they were created. Three distinct domains, Twitter feeds, blog entries, and Reddit comments, are explored in this work. We determine that state-of-the-art methods in stylometry do not perform as well in cross-domain situations (34.3% accuracy) as they do in in-domain situations (83.5% accuracy) and propose methods that improve performance in the cross-domain setting with both feature and classification level techniques which can increase accuracy to up to 70%. In addition to testing these approaches on a large real world dataset, we also examine real world adversarial cases where an author is actively attempting to hide their identity. Being able to identify authors across domains facilitates linking identities across the Internet making this a key security and privacy concern; users can take other measures to ensure their anonymity, but due to their unique writing style, they may not be as anonymous as they believe.

scholarly journals PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Actual Situation ◽  
Controlled Experiments ◽  
Biomedical Image ◽  
Segmentation Methods ◽  
Art Performance

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

scholarly journals Supervised learning for the detection of negation and of its scope in French and Brazilian Portuguese biomedical corpora

2020 ◽  
pp. 1-21 ◽  
Author(s):  
Clément Dalloux ◽  
Vincent Claveau ◽  
Natalia Grabar ◽  
Lucas Emanuel Silva Oliveira ◽  
Claudia Maria Cabral Moro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Extraction ◽  
State Of The Art ◽  
Automatic Detection ◽  
Brazilian Portuguese ◽  
Supervised Machine Learning ◽  
Biomedical Domain ◽  
Learning Approaches ◽  
Cross Domain ◽  
Automatic Methods

Abstract Automatic detection of negated content is often a prerequisite in information extraction systems in various domains. In the biomedical domain especially, this task is important because negation plays an important role. In this work, two main contributions are proposed. First, we work with languages which have been poorly addressed up to now: Brazilian Portuguese and French. Thus, we developed new corpora for these two languages which have been manually annotated for marking up the negation cues and their scope. Second, we propose automatic methods based on supervised machine learning approaches for the automatic detection of negation marks and of their scopes. The methods show to be robust in both languages (Brazilian Portuguese and French) and in cross-domain (general and biomedical languages) contexts. The approach is also validated on English data from the state of the art: it yields very good results and outperforms other existing approaches. Besides, the application is accessible and usable online. We assume that, through these issues (new annotated corpora, application accessible online, and cross-domain robustness), the reproducibility of the results and the robustness of the NLP applications will be augmented.

scholarly journals Contour Detection for Fibre of Preserved Szechuan Pickle Based on Dilated Convolution

2019 ◽  
Vol 9 (13) ◽  
pp. 2684 ◽  
Author(s):  
Hongyang Li ◽  
Lizhuang Liu ◽  
Zhenqi Han ◽  
Dan Zhao
Keyword(s):  
Edge Detection ◽  
State Of The Art ◽  
Contour Detection ◽  
Experimental Results ◽  
Contour Method ◽  
Class Differences ◽  
Dilated Convolution ◽  
The Mean ◽  
Art Performance

Peeling fibre is an indispensable process in the production of preserved Szechuan pickle, the accuracy of which can significantly influence the quality of the products, and thus the contour method of fibre detection, as a core algorithm of the automatic peeling device, is studied. The fibre contour is a kind of non-salient contour, characterized by big intra-class differences and small inter-class differences, meaning that the feature of the contour is not discriminative. The method called dilated-holistically-nested edge detection (Dilated-HED) is proposed to detect the fibre contour, which is built based on the HED network and dilated convolution. The experimental results for our dataset show that the Pixel Accuracy (PA) is 99.52% and the Mean Intersection over Union (MIoU) is 49.99%, achieving state-of-the-art performance.

scholarly journals Towards the cross-domain interoperability of IoT platforms

2016 ◽  
Author(s):  
Sergios Soursos ◽  
Ivana Podnar Zarko ◽  
Patrick Zwickl ◽  
Ivan Gojmerac ◽  
Giuseppe Bianchi ◽  
...  
Keyword(s):  
Cross Domain ◽  
Iot Platforms ◽  
The Cross

Pruning by leveraging training dynamics

2021 ◽  
pp. 1-21
Author(s):  
Andrei C. Apostol ◽  
Maarten C. Stol ◽  
Patrick Forré
Keyword(s):  
State Of The Art ◽  
Object Classification ◽  
Compression Technique ◽  
Pruning Method ◽  
Model Compression ◽  
Art Performance

We propose a novel pruning method which uses the oscillations around 0, i.e. sign flips, that a weight has undergone during training in order to determine its saliency. Our method can perform pruning before the network has converged, requires little tuning effort due to having good default values for its hyperparameters, and can directly target the level of sparsity desired by the user. Our experiments, performed on a variety of object classification architectures, show that it is competitive with existing methods and achieves state-of-the-art performance for levels of sparsity of 99.6 % and above for 2 out of 3 of the architectures tested. Moreover, we demonstrate that our method is compatible with quantization, another model compression technique. For reproducibility, we release our code at https://github.com/AndreiXYZ/flipout.

Applying the FEW nexus concept at the local scale

2021 ◽  
Author(s):  
Henry P. Huntington ◽  
Jennifer Schmidt ◽  
Philip A. Loring ◽  
Erin Whitney ◽  
Srijan Aggarwal ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Local Scale ◽  
Theory And Practice ◽  
Food Energy ◽  
Cross Domain ◽  
Rural Alaska ◽  
Domain Interactions ◽  
The Cross

The food-energy-water (FEW) nexus describes interactions among domains that yield gains or tradeoffs when analyzed together rather than independently. In a project about renewable energy in rural Alaska communities, we applied this concept to examine the implications for sustainability and resilience. The FEW nexus provided a useful framework for identifying the cross-domain benefits of renewable energy, including gains in FEW security. However, other factors such as transportation and governance also play a major role in determining FEW security outcomes in rural Alaska. Here we show the implications of our findings for theory and practice. The precise configurations of and relationships among FEW nexus components vary by place and time, and the range of factors involved further complicates the ability to develop a functional, systematic FEW model. Instead, we suggest how the FEW nexus may be applied conceptually to identify and understand cross-domain interactions that contribute to long-term sustainability and resilience.

Study on the Mechanism of Water Resources Cultural under the Perspective of Cross-Domain Consultation Management

2014 ◽  
Vol 955-959 ◽  
pp. 3145-3150
Author(s):  
Xian Ze Peng ◽  
Cai Yuan ◽  
Qian Yu
Keyword(s):  
Water Management ◽  
Water Pollution ◽  
Water Resource Management ◽  
Control Mechanism ◽  
Water Environment ◽  
Water Culture ◽  
Cross Domain ◽  
Rivers And Lakes ◽  
The Cross ◽  
The Government

Along with the main rivers and lakes of China are polluted inordinately, water environment issues in China have been becomingincreasingly severe. The cross-domain water pollution contradictions cannot be well settled by the government-centered river and watercourse control, which means that,urgently, a new river and watercourse control mechanism needs to be established. With continuous changes of water management, in order to get along with water even more harmoniously, the mankind has formed the concept of water resource management through cross-domain consultation. Combining traditional, historical and social culturefactors, ancient and modern, this paper analyzes influences of the cross-domain consultation upon water culture, proposes detailed countermeasures of establishing the water culture featuring“harmoniousco-existence between mankind and water”by cross-domain consultation, so as to effectively settle contradictions triggered by water pollution amongdifferent administrative regions, and improve the efficiency ofwatercontrol.

Sign in / Sign up

Export Citation Format

Share Document