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Coronavirus Disease 2019 (COVID-19) has been affecting most of the countries and impacting almost every aspect of people's lives. More than one hundred million confirmed cases and two million deaths have been reported due to COVID-19 as of February 2021. While our society suffers an unanticipated epidemic, researchers and engineers have developed various technologies to manage this global emergency

In this article, we propose a face spoofing detection method by learning to fuse high-frequency (HF) and low-frequency (LF) features, in an effort to improve the generalization capability and fill up the domain gap between training and testing when the antispoofing is practically conducted in unseen scenarios. In particular, the proposed face antispoofing model consists of two streams that extract

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Political statements are carefully crafted to garner public support for a particular ideology. These statements are often biased and sometimes misleading. Separating fact from fiction has proven to be a difficult task, generally accomplished by cross-checking political statements against an impartial and trustworthy news source. In this article, we make three contributions. First, we compile a novel

Classically, the style of the generated music by deep learning models is usually governed by the training dataset. In this article, we improved this by proposing the continuous style embedding ${z}_{s}$ to the general formulation of variational autoencoder (VAE) to allow users to be able to condition on the style of the generated music. For this purpose, we explored and compared two different methods

In this article, we focus on a new concept called multichannel steganography, in which a sender is able to transmit different secret data to multiple receivers via the same cover image. First, we propose a general framework to achieve multichannel steganography based on modern steganographic paradigm. Then, we design two schemes to, respectively, achieve separable and sequential multichannel steganography

An ideal audio retrieval system identifies a short query snippet from a massive audio database with both robustness and efficiency. Unfortunately, none of the existing systems could robustly handle all distortions while being efficient. Enhanced sampling and counting (eSC), the state-of-the-art audio retrieval method proposed for Philips-like fingerprints, has achieved both high efficiency and strong

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The thirteen papers in this special section focus on social media data analytics with deep intelligence. Big cross-model social media data analytics with deep intelligence aims to handle data sampling from multimodal deep spaces, so as to well characterize the big data. The addressed topic span from the range of human action recognition to affective computing, disaster detection, classification, retrieval

With the current global outbreak of COVID-19, an increasing number of people are suffering from negative mental states and mental disorders. We propose a multimodal psychological computational technology in a universal environment. We establish a mental health database following a naturalistic paradigm as well as a long-term ubiquitous interpretable psychological computing model based on prior knowledge

In this article, we develop an end-to-end clothing collocation learning framework based on a bidirectional long short-term memories (Bi-LTSM) model, and propose new feature extraction and fusion modules. The feature extraction module uses Inception V3 to extract low-level feature information and the segmentation branches of Mask Region Convolutional Neural Network (RCNN) to extract high-level semantic

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Each part of a nonuniform distorted video (NUDV) has a unique distortion degree. When NUDV blocks are used as inputs, traditional machine-learning-based video quality assessment (VQA) methods frequently do not work effectively. Because these methods directly assign the label of the entire video to blocks, causing the unreliability of labels. We creatively propose video bag, a collection of video blocks

In this article, we propose an end-to-end real-time stereo matching network (RTSMNet). RTSMNet consists of three modules. The global and local feature extraction (GLFE) module captures the hierarchical context information and generates the coarse cost volume. The initial disparity estimation module is a compact three-dimensional convolution architecture aiming to produce the low-resolution (LR) disparity

This study aims to analyze and determine the factors that have an impact on the intensive use of Instagram and its relationship with smartphone addiction and self-esteem. A total of 389 Instagram users aged between 18 and 57 (M = 23.98; SD = 5.37) completed an online survey based on three standardized scales. The findings of the study suggest that there are statistically significant differences in

Microexpressions are hard to spot due to fleeting and involuntary moments of facial muscles. Interpretation of microemotions from video clips is a challenging task. In this article, we propose an affective-motion imaging that cumulates rapid and short-lived variational information of microexpressions into a single response. Moreover, we have proposed an AffectiveNet: Affective-motion feature learning

With the rapid growth of Android applications, there is an urgent need for powerful Android malware detection technology nowadays. Existing classification models can be summarized with the following two steps-feature extraction and classification model learning. To further enhance the representation ability of existing classification models, this article presents an Android malicious application detection

In multilabel text classification, the label information of training instances may be incomplete due to various factors, such as expensively manual labeling, errors, etc. Hence, imposing big challenges to training classifiers. To tackle the problem of the incomplete labels, we proposed to exploit label correlations, and further jointly leverage manifold regularization and label confidence constraint

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The papers in this special section focus on the topic of urban multimedia computing. Presents emerging methods in multimedia computing for urban analysis and applications. Urban computing is a process of acquisition, integration, analysis, and understanding of the urban data generated from various sources, e.g., sensors, devices, and social media, for various applications. Multimedia computing plays

Self-driving cars leverage on semantic segmentation to understand an urban scene. However, it is costly to collect segmentation labels, thus, synthetic datasets are used to train segmentation models. Unfortunately, the synthetic to real domain shift causes these models to perform poorly. Prior works use adversarial training to align features of both synthetic and real-world images. We observe that

8 k ultra-high definition (UHD) is paving the way for the next-generation video systems. In the audiovisual industry, besides delivering a more immersive experience, it is a mean to smooth spatial artifacts during video sampling. In the medical industry, it may provide surgeons with increased reality in surgeries such as endoscopy. Nevertheless, researchers are struggling to meet the high throughput

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Human action recognition is the basis technology of human behavior understanding, and it is a research hotspot in the field of computer vision. Recently, some studies show skeleton data (i.e., joint points and edges) is naturally more conducive to mining the connotation of human action, so exploring the relationship between joints and bones is helpful to improve action recognition. In this article

Multiview clustering has attracted much attention due to its ability to aggregate various source information and many advanced approaches have been proposed in the literature. However, there are still two major issues that need to be further explored: i) how to efficiently handle large-scale data; ii) how to effectively incorporate the complementary multiple sources. In this article, we fulfill a unified

Image retrieval on the social media platforms propels the tourism information sharing on the Internet. Existing image retrieval methods lack the capability of discovering global statistical distribution of feature representations at multiple scales. In this article, we propose a gated multiscale NetVLAD network, which constructs feature pyramid network based on ResNet backbone and computes NetVLAD

Cross-modal retrieval has become a hot issue in past years. Many existing works pay attentions on correlation learning to generate a common subspace for cross-modal correlation measurement, and others use adversarial learning technique to abate the heterogeneity of multimodal data. However, very few works combine correlation learning and adversarial learning to bridge the intermodal semantic gap and

With the increasing popularity of online shopping, searching for products with images for item retrieval has gradually become an effective approach. This trend is especially evident in the fashion industry. In common media, clothing items are usually worn on the human body. They can be straightforwardly segmented from the source media by utilizing detection or parsing algorithms. However, this may

Recently, during natural disasters, the use of social media as a source of actionable information has increased significantly. Finding disaster-related tweets and analyzing their textual content and images can help government agencies and rescue organizations make better decisions. The main challenge of disaster-related message detection on social networking sites is how to identify posts related to

Synchronization of sensory effects with multimedia content is a nontrivial and error-prone task that can discourage authoring of mulsemedia applications. Although there are authoring tools that perform some automatic authoring of sensory effect metadata, the analysis techniques that they use are not generally enough to identify complex components that may be related to sensory effects. In this article

FU-PCM is an effective clustering mode by inducing the regularization constraint in C-means to avoid the interference of noises and outliers. However, it is difficult to obtain the satisfactory performance in the context of imbalance category distribution. To address above problem, first, we propose a novel clustering model, which exploits Pearson Correlation Coefficient to auto-balance the optimal

To evaluate the driving behavior of unmanned vehicles, the testing of driving algorithms using urban road images is necessary. In this article, we propose a framework using generative adversarial networks (GANs) with structural information for image style transfer: StructureGAN and GradientGAN. Different types of urban image transfers are generated using the proposed framework, such as day to night

In this article, an effective approach is proposed for nonrigid structure from motion to deal with the image sequence with a complex deformation. In the proposed method, a three-dimensional (3-D) structure is first divided into multiple local trajectory groups by utilizing the distance weights of the pairwise points. For each local trajectory group, a weak estimator is constructed by means of the kernel

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