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With the growth of crowd phenomena in the real world, crowd scene understanding is becoming an important task in anomaly detection and public security. Visual ambiguities and occlusions, high density, low mobility and scene semantics, however, make this problem a great challenge. In this paper, we propose an end-to-end deep architecture, Convolutional DLSTM (ConvDLSTM), for crowd scene understanding...
In nowadays, as the development of digital photographic technology, video files grow rapidly, there is a great demand for automatic video semantic analysis in many scenes, such as video semantic understanding, content-based analysis, video retrieval. Shot boundary detection is a key basic technology and first step for video analysis. However, recent methods are time consuming and performs bad in the...
We propose an image-text alignment framework to match images with text, and take blog article summarization as the main application. Objects in an image are first detected, from them deep features are extracted and transformed into a space commonly shared with the text. On the other hand, sentences of a blog article are represented as vectors, and are also embedded into the common space. With these...
Convolutional neural network (CNN) has drawn increasing interest in visual tracking, among which fully-convolutional Siamese network based method (SiamFC) is quite popular due to its competitive performance in both precision and efficiency. Generally, SiamFC captures robust semantics from high-level features in the last layer but ignores detailed spatial features in earlier layers, thus tending to...
Trigger detection plays a key role in the extraction of biomedical events, so it will influence the results of biomedical events extraction directly. The traditional biomedical event trigger recognition method is based on artificial design features and construct feature vectors; Not only does it consume great amounts of manpower, it also lacks system generalization ability. Most of methods of trigger...
Data mapping among different data standards in health institutes is often a necessity when data exchanges occur among different institutes. However, no matter rule-based approaches or traditional machine learning methods, none of these methods have achieved satisfactory results yet. In this work, we propose a deep learning method, mixture feature embedding convolutional neural network (MfeCNN), to...
Opioid (e.g., heroin and morphine) addiction has become one of the largest and deadliest epidemics in the United States. To combat such deadly epidemic, there is an urgent need for novel tools and methodologies to gain new insights into the behavioral processes of opioid addiction and treatment. In this paper, we design and develop an intelligent system named iOPU to automate the detection of opioid...
On electronic game platforms, different payment transactions have different levels of risk. Risk is generally higher for digital goods in e-commerce. However, it differs based on product and its popularity, the offer type (packaged game, virtual currency to a game or subscription service), storefront and geography. Existing fraud policies and models make decisions independently for each transaction...
In this paper we propose a deep learning technique to improve the performance of semantic segmentation tasks. Previously proposed algorithms generally suffer from the over-dependence on a single modality as well as a lack of training data. We made three contributions to improve the performance. Firstly, we adopt two models which are complementary in our framework to enrich field-of-views and features...
Extracting stop purpose information from raw GPS data is a crucial task in most location-aware applications. With the continuous growth of GPS data collected from mobile devices, this task is becoming more and more interesting; a lot of recent research has focused on pedestrians (mobile phones) data, while the commercial vehicles sector is almost unexplored. In this paper we target the problem of...
The standard dementia screening tool Mini Mental State Examination (MMSE) and the standard dementia staging tool Clinical Dementia Rating Scale (CDR) are prominent methods for answering questions whether a person might have dementia and about the dementia severity respectively. These methods are time consuming and require well-educated personnel to administer. Conversely, cognitive tests, such as...
Because of the crisis of unexpected events, data sources are complex and diverse. The application of the phrase weight measurement technique and the network user free marking technology in large data technology, transform the multimodal crisis information into a single information source, An integrated model for the extraction of crisis information was established. The integrative course includes...
Currently, open source projects receive various kinds of issues daily, because of the extreme openness of Issue Tracking System (ITS) in GitHub. ITS is a labor-intensive and time-consuming task of issue categorization for project managers. However, a contributor is only required a short textual abstract to report an issue in GitHub. Thus, most traditional classification approaches based on detailed...
Understanding user query intent is a crucial task to Question-Answering area. With the development of online health services, online health communities generate huge amount of valuable medical Question-Answering data, where user intention can be mined. However, the queries posted by common users have many domain concepts and colloquial expressions, which make the understanding of user intents very...
Salient object detection has been greatly boosted thanks to the deep convolutional neural networks (CNN), especially fully convolutional neural networks (FCN). Nowadays, it is possible to train an end-to-end deep model for salient object detection. However, the diverse scales of salient objects still pose major challenges for these state-of-the-art methods. In this paper, we investigate how different...
Text classification (TC) is a task that assigns a text to one or more classes and predefined categories. Constructing text classifiers with high accuracy is a vital task in biomedical field, given the wealth of information hidden in unlabelled documents. Because of large feature spaces, traditionally discriminative approaches, such as logistic regression and support vector machines with n-gram and...
One of the promising new directions for Content Based Video Retrieval is object based retrieval which allows the user to manipulate video object as a part of searching and browsing. The major obstacle for the use of objects in video retrieval is the appropriate representation of objects in video database. The purpose of this work is to present an object based framework consisting of entire processing...
Semantic parsing of large-scale 3D point clouds is an important research topic in computer vision and remote sensing fields. Most existing approaches utilize hand-crafted features for each modality independently and combine them in a heuristic manner. They often fail to consider the consistency and complementary information among features adequately, which makes them difficult to capture high-level...
In this paper, we propose a novel single image action recognition algorithm based on the idea of semantic part actions. Unlike existing part-based methods, we argue that there exists a mid-level semantic, the semantic part action; and human action is a combination of semantic part actions and context cues. In detail, we divide human body into seven parts: head, torso, arms, hands and lower body. For...
Referring expression is a kind of language expression that used for referring to particular objects. To make the expression without ambiguation, people often use attributes to describe the particular object. In this paper, we explore the role of attributes by incorporating them into both referring expression generation and comprehension. We first train an attribute learning model from visual objects...
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