The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Recently, the community of style transfer is trying to incorporate semantic information into traditional system. This practice achieves better perceptual results by transferring the style between semantically-corresponding regions. Yet, few efforts are invested to address the computation bottleneck of back-propagation. In this paper, we propose a new framework for fast semantic style transfer. Our...
Textual-visual matching aims at measuring similarities between sentence descriptions and images. Most existing methods tackle this problem without effectively utilizing identity-level annotations. In this paper, we propose an identity-aware two-stage framework for the textual-visual matching problem. Our stage-1 CNN-LSTM network learns to embed cross-modal features with a novel Cross-Modal Cross-Entropy...
Leveraging class semantic descriptions and examples of known objects, zero-shot learning makes it possible to train a recognition model for an object class whose examples are not available. In this paper, we propose a novel zero-shot learning model that takes advantage of clustering structures in the semantic embedding space. The key idea is to impose the structural constraint that semantic representations...
We present a general approach to video understanding, inspired by semantic transfer techniques that have been successfully used for 2D image analysis. Our method considers a video to be a 1D sequence of clips, each one associated with its own semantics. The nature of these semantics – natural language captions or other labels – depends on the task at hand. A test video is processed by forming correspondences...
This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. The paper considers the possibility and necessity of using in modern control and training systems with a natural language interface methods and mechanisms, characteristic for knowledge processing systems. This symbiosis assumes the introduction of specialized...
Semantic similarity and relatedness are applied more and more extensively in many fields, such as in Artificial Intelligence, Semantic Web and Knowledge Management. In this paper, we propose a comprehensive metric of similarity, a method of relatedness measure and a comprehensive degree measure that combines semantic similarity and relatedness between two concepts. Then we compare the proposed metrics...
Scene recognition is an important and challenging problem in the field of computer vision owing to the variations in the same class and the similarities between different classes. This paper presents a novel approach that learns a reasonable dictionary from convolutional features to effectively describe the distinctive and shared properties in scene images. Substantial convolution operations in Deep...
We present a new deep learning-based approach for dense stereo matching. Compared to previous works, our approach does not use deep learning of pixel appearance descriptors, employing very fast classical matching scores instead. At the same time, our approach uses a deep convolutional network to predict the local parameters of cost volume aggregation process, which in this paper we implement using...
Paraphrase Detection is the task of examining if two sentences convey the same meaning or not. Here, in this paper, we have chosen a sentence embedding by unsupervised RAE vectors for capturing syntactic as well as semantic information. The RAEs learn features from the nodes of the parse tree and chunk information along with unsupervised word embedding. These learnt features are used for measuring...
We present a procedure for generating Abstract Meaning Representation (AMR) structures from English sentences based on a transition-based system. Our proposed solution makes use of Long Short Term Memory networks to learn the action sequence that needs to be applied on the sentence in order to obtain the AMR graph. The action set is an extension of the arc-standard dependency parser, with several...
Tagging provides a convenient means to assign tokens of identification to research papers which facilitate recommendation, search and disposition process of research papers. This paper contributes a document centered approach for auto-tagging of research papers. The auto-tagging method mainly comprises of two processes:- classification and tag selection. The classification process involves automatic...
Coreference resolution plays a significant role in natural language processing systems. It is the method of figuring out all the noun phrases that refer back to the identical real world entity. Several researches have been done in noun phrase coreference resolution by using certain machine learning techniques. Our paper proposes a machine learning approach using support vector machines (SVM) towards...
Multimedia semantic concept detection is one of the major research topics in multimedia data analysis in recent years. Disaster information management needs the assistance of multimedia data analysis to better utilize those disasterrelated information, which has been widely shared by people through the Internet. In this paper, a Feature Affinity based Multiple Correspondence Analysis and Decision...
Video summarization (VS) is one of key video signal processing techniques for unmanned aerial vehicles (UAVs). Essentially VS aims at eliminating redundant frames in aerial videos (AVs) with high similarity, which is helpful for quick browsing, retrieving and efficient storage without losing important information. For VS technique, how to measure the similarity between video frames is not a trivial...
In this paper, we propose a method for extracting ICD-10 codes from the natural language description of a patient illness complaint. The proposed method is based on distributional semantics of terms that appeared in the two natural language expressions: a patient's complaint and an ICD-10 code description. In order to locate the relevant fragment of words within a given long and noisy patient's expression,...
In recent years, image generation using Convolutional Neural Networks (CNNs) has become increasingly popular in the computer vision domain. However, there is less attention on using CNNs for sprite generation for games. A possible reason for this is that the amount of available sprite data in games is significantly less than in other domains, which typically use hundreds of thousands of images, or...
Pedestrian detection and semantic segmentation are highly correlated tasks which can be jointly used for better performance. In this paper, we propose a pedestrian detection method making use of semantic labeling to improve pedestrian detection results. A deep learning based semantic segmentation method is used to pixel-wise label images into 11 common classes. Semantic segmentation results which...
Customer reviews, a.k.a. word-of-mouth reviews, have been important resources of information for text mining. They naturally include both positive and negative opinions on the products or services, as well as neutral observations helpful for everyone who is about to purchase the products or about to decide what to do with the product or the service. Among many customer reviews, we focus on cosmetic...
With the rapid development of Internet, how to obtain valuable information from massive messages has become a major problem we need to be solved in the information explosive era. This paper introduces the development route of information extraction technology, and discusses four categories of Chinese entity relation extraction technologies in depth. Finally, the advantages and disadvantages of different...
This paper presents the results of systematic and comparative experimentation with major types of methodologies for automatic duplicate question detection when these are applied to datasets of progressively larger sizes, thus allowing to study the learning profiles of this task under these different approaches and evaluate their merits. This study was made possible by resorting to the recent release...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.