scholarly journals Norm-guided Adaptive Visual Embedding for Zero-Shot Sketch-Based Image Retrieval

2021 ◽  
Author(s):  
Wenjie Wang ◽  
Yufeng Shi ◽  
Shiming Chen ◽  
Qinmu Peng ◽  
Feng Zheng ◽  
...  
Keyword(s):  
Image Retrieval ◽  
State Of The Art ◽  
Visual Similarity ◽  
Language Models ◽  
Visual Appearance ◽  
Common Space ◽  
Great Progress ◽  
Real World Applications ◽  
The Common

Zero-shot sketch-based image retrieval (ZS-SBIR), which aims to retrieve photos with sketches under the zero-shot scenario, has shown extraordinary talents in real-world applications. Most existing methods leverage language models to generate class-prototypes and use them to arrange the locations of all categories in the common space for photos and sketches. Although great progress has been made, few of them consider whether such pre-defined prototypes are necessary for ZS-SBIR, where locations of unseen class samples in the embedding space are actually determined by visual appearance and a visual embedding actually performs better. To this end, we propose a novel Norm-guided Adaptive Visual Embedding (NAVE) model, for adaptively building the common space based on visual similarity instead of language-based pre-defined prototypes. To further enhance the representation quality of unseen classes for both photo and sketch modality, modality norm discrepancy and noisy label regularizer are jointly employed to measure and repair the modality bias of the learned common embedding. Experiments on two challenging datasets demonstrate the superiority of our NAVE over state-of-the-art competitors.

scholarly journals Live Broadcasting of High Definition Audiovisual Content Using HDTV over Broadband IP Networks

2008 ◽  
Vol 2008 ◽  
pp. 1-18 ◽  
Author(s):  
C. E. Vegiris ◽  
K. A. Avdelidis ◽  
C. A. Dimoulas ◽  
G. V. Papanikolaou
Keyword(s):  
Quality Of Experience ◽  
State Of The Art ◽  
Ip Networks ◽  
Telecommunication Networks ◽  
High Fidelity ◽  
High Definition ◽  
Broadband Internet ◽  
Real World Applications ◽  
Audio Video

The current paper focuses on validating an implementation of a state-of-the art audiovisual (AV) technologies setup for live broadcasting of cultural shows, via broadband Internet. The main objective of the work was to study, configure, and setup dedicated audio-video equipment for the processes of capturing, processing, and transmission of extended resolution and high fidelity AV content in order to increase realism and achieve maximum audience sensation. Internet2 and GEANT broadband telecommunication networks were selected as the most applicable technology to deliver such traffic workloads. Validation procedures were conducted in combination with metric-based quality of service (QoS) and quality of experience (QoE) evaluation experiments for the quantification and the perceptual interpretation of the quality achieved during content reproduction. The implemented system was successfully applied in real-world applications, such as the transmission of cultural events from Thessaloniki Concert Hall throughout Greece as well as the reproduction of Philadelphia Orchestra performances (USA) via Internet2 and GEANT backbones.

scholarly journals Unified Embedding Alignment with Missing Views Inferring for Incomplete Multi-View Clustering

2019 ◽  
Vol 33 ◽  
pp. 5393-5400 ◽  
Author(s):  
Jie Wen ◽  
Zheng Zhang ◽  
Yong Xu ◽  
Bob Zhang ◽  
Lunke Fei ◽  
...  
Keyword(s):  
Real World ◽  
Local Structure ◽  
Semantic Information ◽  
State Of The Art ◽  
Multiple Views ◽  
Learning Problem ◽  
Graph Regularization ◽  
Real World Applications ◽  
Weighting Strategy ◽  
The Common

Multi-view clustering aims to partition data collected from diverse sources based on the assumption that all views are complete. However, such prior assumption is hardly satisfied in many real-world applications, resulting in the incomplete multi-view learning problem. The existing attempts on this problem still have the following limitations: 1) the underlying semantic information of the missing views is commonly ignored; 2) The local structure of data is not well explored; 3) The importance of different views is not effectively evaluated. To address these issues, this paper proposes a Unified Embedding Alignment Framework (UEAF) for robust incomplete multi-view clustering. In particular, a locality-preserved reconstruction term is introduced to infer the missing views such that all views can be naturally aligned. A consensus graph is adaptively learned and embedded via the reverse graph regularization to guarantee the common local structure of multiple views and in turn can further align the incomplete views and inferred views. Moreover, an adaptive weighting strategy is designed to capture the importance of different views. Extensive experimental results show that the proposed method can significantly improve the clustering performance in comparison with some state-of-the-art methods.

scholarly journals Unsupervised Quality Estimation for Neural Machine Translation

2020 ◽  
Vol 8 ◽  
pp. 539-555
Author(s):  
Marina Fomicheva ◽  
Shuo Sun ◽  
Lisa Yankovskaya ◽  
Frédéric Blain ◽  
Francisco Guzmán ◽  
...  
Keyword(s):  
Machine Translation ◽  
Real World ◽  
State Of The Art ◽  
Black Box ◽  
Test Time ◽  
Quality Estimation ◽  
Neural Machine Translation ◽  
Real World Applications ◽  
Unsupervised Approach

Quality Estimation (QE) is an important component in making Machine Translation (MT) useful in real-world applications, as it is aimed to inform the user on the quality of the MT output at test time. Existing approaches require large amounts of expert annotated data, computation, and time for training. As an alternative, we devise an unsupervised approach to QE where no training or access to additional resources besides the MT system itself is required. Different from most of the current work that treats the MT system as a black box, we explore useful information that can be extracted from the MT system as a by-product of translation. By utilizing methods for uncertainty quantification, we achieve very good correlation with human judgments of quality, rivaling state-of-the-art supervised QE models. To evaluate our approach we collect the first dataset that enables work on both black-box and glass-box approaches to QE.

scholarly journals Multi-View Consistency for Relation Extraction via Mutual Information and Structure Prediction

2020 ◽  
Vol 34 (05) ◽  
pp. 9106-9113
Author(s):  
Amir Veyseh ◽  
Franck Dernoncourt ◽  
My Thai ◽  
Dejing Dou ◽  
Thien Nguyen
Keyword(s):  
Structure Prediction ◽  
State Of The Art ◽  
Relation Extraction ◽  
Semantic Relations ◽  
General Strategy ◽  
Novel Method ◽  
Dependency Trees ◽  
The Common ◽  
The One

Relation Extraction (RE) is one of the fundamental tasks in Information Extraction. The goal of this task is to find the semantic relations between entity mentions in text. It has been shown in many previous work that the structure of the sentences (i.e., dependency trees) can provide important information/features for the RE models. However, the common limitation of the previous work on RE is the reliance on some external parsers to obtain the syntactic trees for the sentence structures. On the one hand, it is not guaranteed that the independent external parsers can offer the optimal sentence structures for RE and the customized structures for RE might help to further improve the performance. On the other hand, the quality of the external parsers might suffer when applied to different domains, thus also affecting the performance of the RE models on such domains. In order to overcome this issue, we introduce a novel method for RE that simultaneously induces the structures and predicts the relations for the input sentences, thus avoiding the external parsers and potentially leading to better sentence structures for RE. Our general strategy to learn the RE-specific structures is to apply two different methods to infer the structures for the input sentences (i.e., two views). We then introduce several mechanisms to encourage the structure and semantic consistencies between these two views so the effective structure and semantic representations for RE can emerge. We perform extensive experiments on the ACE 2005 and SemEval 2010 datasets to demonstrate the advantages of the proposed method, leading to the state-of-the-art performance on such datasets.

scholarly journals Connecting Images through Sources: Exploring Low-Data, Heterogeneous Instance Retrieval

2021 ◽  
Vol 13 (16) ◽  
pp. 3080
Author(s):  
Dimitri Gominski ◽  
Valérie Gouet-Brunet ◽  
Liming Chen
Keyword(s):  
Image Retrieval ◽  
State Of The Art ◽  
Feature Learning ◽  
Training Dataset ◽  
Diffusion Method ◽  
Cross Domain ◽  
Real World Applications ◽  
Promising Solution ◽  
Domain Performance ◽  
Instance Retrieval

Along with a new volume of images containing valuable information about our past, the digitization of historical territorial imagery has brought the challenge of understanding and interconnecting collections with unique or rare representation characteristics, and sparse metadata. Content-based image retrieval offers a promising solution in this context, by building links in the data without relying on human supervision. However, while the latest propositions in deep learning have shown impressive results in applications linked to feature learning, they often rely on the hypothesis that there exists a training dataset matching the use case. Increasing generalization and robustness to variations remains an open challenge, poorly understood in the context of real-world applications. Introducing the alegoria benchmark, containing multi-date vertical and oblique aerial digitized photography mixed with more modern street-level pictures, we formulate the problem of low-data, heterogeneous image retrieval, and propose associated evaluation setups and measures. We propose a review of ideas and methods to tackle this problem, extensively compare state-of-the-art descriptors and propose a new multi-descriptor diffusion method to exploit their comparative strengths. Our experiments highlight the benefits of combining descriptors and the compromise between absolute and cross-domain performance.

scholarly journals Quality Matters: Assessing cQA Pair Quality via Transductive Multi-View Learning

2018 ◽  
Author(s):  
Xiaochi Wei ◽  
Heyan Huang ◽  
Liqiang Nie ◽  
Fuli Feng ◽  
Richang Hong ◽  
...  
Keyword(s):  
Regression Models ◽  
Question Answering ◽  
Community Based ◽  
Transductive Learning ◽  
Common Space ◽  
Proposed Model ◽  
Rich Information ◽  
The Common ◽  
Real World Datasets

Community-based question answering (cQA) sites have become important knowledge sharing platforms, as massive cQA pairs are archived, but the uneven quality of cQA pairs leaves information seekers unsatisfied. Various efforts have been dedicated to predicting the quality of cQA contents. Most of them concatenate different features into single vectors and then feed them into regression models. In fact, the quality of cQA pairs is influenced by different views, and the agreement among them is essential for quality assessment. Besides, the lacking of labeled data significantly hinders the quality prediction performance. Toward this end, we present a transductive multi-view learning model. It is designed to find a latent common space by unifying and preserving information from various views, including question, answer, QA relevance, asker, and answerer. Additionally, rich information in the unlabeled test cQA pairs are utilized via transductive learning to enhance the representation ability of the common space. Extensive experiments on real-world datasets have well-validated the proposed model.

Analysis of color Image Enhancement Using DWT, Wavelet Shrinkageand FHE Methods

2017 ◽  
Vol 7 (8) ◽  
pp. 56
Author(s):  
Ashish Dwivedi ◽  
Nirupma Tiwari
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Human Perception ◽  
Histogram Equalization ◽  
Wavelet Shrinkage ◽  
Visual Appearance ◽  
Enhancement Method ◽  
Fuzzy Histogram ◽  
Rgb Image

Image enhancement (IE) is very important in the field where visual appearance of an image is the main. Image enhancement is the process of improving the image in such a way that the resulting or output image is more suitable than the original image for specific task. With the help of image enhancement process the quality of image can be improved to get good quality images so that they can be clear for human perception or for the further analysis done by machines.Image enhancement method enhances the quality, visual appearance, improves clarity of images, removes blurring and noise, increases contrast and reveals details. The aim of this paper is to study and determine limitations of the existing IE techniques. This paper will provide an overview of different IE techniques commonly used. We Applied DWT on original RGB image then we applied FHE (Fuzzy Histogram Equalization) after DWT we have done the wavelet shrinkage on Three bands (LH, HL, HH). After that we fuse the shrinkage image and FHE image together and we get the enhance image.

The concept of motivation for effective credit risk management

2020 ◽  
Vol 26 (11) ◽  
pp. 2567-2593
Author(s):  
M.V. Pomazanov
Keyword(s):  
Risk Management ◽  
Economic Modeling ◽  
Type I ◽  
Credit Risk Management ◽  
Curve Modeling ◽  
Business Functions ◽  
The Common ◽  
Lending Decisions ◽  
The One

Subject. The study addresses the improvement of risk management efficiency and the quality of lending decisions made by banks. Objectives. The aim is to present the bank management with a fair algorithm for risk management motivation on the one hand, and the credit management (business) on the other hand. Within the framework of the common goal to maximize risk-adjusted income from loans, this algorithm will provide guidelines for ‘risk management’ and ‘business’ functions on how to improve individual and overall efficiency. Methods. The study employs the discriminant analysis, type I and II errors, Lorentz curve modeling, statistical analysis, economic modeling. Results. The paper offers a mechanism for assessing the quality of risk management decisions as opposed to (or in support of) decisions of the lending business when approving transactions. The mechanism rests on the approach of stating type I and II errors and the corresponding classical metric of the Gini coefficient. On the ‘business’ side, the mechanism monitors the improvement or deterioration of the indicator of changes in losses in comparison with the market average. Conclusions. The study substantiates the stimulating ‘rules of the game’ between the ‘business’ and ‘risk management’ to improve the efficiency of the entire business, to optimize interactions within the framework of internal competition. It presents mathematical tools to calculate corresponding indicators of the efficiency of internally competing entities.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

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