Search

The antagonistic behavior in the crowd usually exacerbates the seriousness of the situation in sudden riots, where the antagonistic emotional contagion and behavioral decision making play very important roles. However, the complex mechanism of antagonistic emotion influencing decision making, especially in the environment of sudden confrontation, has not yet been explored very clearly. In this paper, we propose an Emotional contagion-aware Deep reinforcement learning model for Antagonistic Crowd Simulation (ACSED). First, we build a group emotional contagion module based on the improved Susceptible Infected Susceptible (SIS) infection disease model, and estimate the emotional state of the group at each time step during the simulation. Then, the tendency of crowd antagonistic action is estimated based on Deep Q Network (DQN), where the agent learns the action autonomously, and leverages the mean field theory to quickly calculate the influence of other surrounding individuals on the central one. Finally, the rationality of the predicted actions by DQN is further analyzed in combination with group emotion, and the final action of the agent is determined. The proposed method in this paper is verified through several experiments with different settings. We can conclude antagonistic emotions play a critical role in the decision making of the crowd through influencing the individual behavior in the riot scenario, where individual behaviors are primarily driven by emotions and goals, rather than common rules. The experiment results also prove that the antagonistic emotion has a vital impact on the group combat, and positive emotional states are more conducive to combat. Moreover, by comparing the simulation results with real scenes, the feasibility of our method is further confirmed, which can provide good reference to formulate battle plans and improve the win rate of righteous groups in a variety of situations. [ABSTRACT FROM AUTHOR]

Existing network embedding algorithms based on generative adversarial networks (GANs) improve the robustness of node embeddings by selecting high-quality negative samples with the generator to play against the discriminator. Since most of the negative samples can be easily discriminated from positive samples in graphs, their poor competitiveness weakens the function of the generator. Inspired by the sales skills in the market, in this article, we present tripartite adversarial training for network embeddings (TriATNE), a novel adversarial learning framework for learning stable and robust node embeddings. TriATNE consists of three players: 1) producer; 2) seller; and 3) customer. The producer strives to learn the representation of each sample (node pair), making it easy for the customer to differentiate between the positive and the negative, while the seller tries to confuse the customer by selecting realistic-looking samples. The customer, a biased evaluation metric, provides feedback for training the producer and the seller. To further enhance the robustness of node embedding, we model the customer as a two-layer neural network, where each unit in the hidden layer can be regarded as a customer with different preferences. TriATNE also plays against the producer by adjusting the weight of each customer. We test the performance of TriATNE on two common tasks: classification as well as link prediction. The experimental results on various publicly available datasets show that TriATNE can exploit the network structure well. [ABSTRACT FROM AUTHOR]

The superiority of deeply learned representations relies on large-scale labeled datasets. However, annotating data are usually expensive or even infeasible in some scenarios. To address this problem, we propose an unsupervised method to leverage instance discrimination and similarity for deep visual representation learning. The method is based on an observation that convolutional neural networks (CNNs) can learn a meaningful visual representation with instancewise classification, in which each instance is treated as an individual class. By this instancewise discriminative learning, instances can reasonably distribute in the representation space, which reveals their similarities. In order to further improve visual representations, we propose a dual-level progressive similar instance selection (DPSIS) method to build a bridge from instance to class by selecting similar instances (neighbors) for each instance (anchor) and treating the anchor and its neighbors as the same class. To be specific, DPSIS adaptively selects two levels of neighbors, that is: 1) an “absolutely similar level” and 2) a “relatively similar level.” Instances in the absolutely similar level are used as hard labels, while instances in the relatively similar level are used as soft labels. Moreover, during training, DPSIS is able to progressively select more neighbors without human supervision. At the beginning of training, because CNNs are weak, most instances are distributed relatively randomly in the representation space and only a few easy-to-recognize instances are selected as neighbors. As CNN models become stronger, the semantic meaning of each instance grows clearer. Those instances originally distributed in a relatively random manner gradually move to meaningful positions. This consequently facilitates CNN training since the number of reliable samples increases. Experiments on seven benchmarks, including three small-scale and two large-scale coarse-grained image classification datasets, and two fine-grained categorization datasets, demonstrate the effectiveness of our DPSIS. Our codes have been released at https://github.com/hehefan/DPSIS. [ABSTRACT FROM AUTHOR]

This paper proposes a new graph convolutional operator called central difference graph convolution (CDGC) for skeleton based action recognition. It is not only able to aggregate node information like a vanilla graph convolutional operation but also gradient information. Without introducing any additional parameters, CDGC can replace vanilla graph convolution in any existing Graph Convolutional Networks (GCNs). In addition, an accelerated version of the CDGC is developed which greatly improves the speed of training. Experiments on two popular large-scale datasets NTU RGB+D 60 & 120 have demonstrated the efficacy of the proposed CDGC. Code is available at https://github.com/iesymiao/CD-GCN. [ABSTRACT FROM AUTHOR]

Network embedding has been widely used to solve the network analytics problem. Existing methods mainly focus on networks with single-layered homogeneous or heterogeneous networks. However, many real-world complex systems can be naturally represented by multilayer networks, which is another term of heterogeneous networks with multiple edge/relation types. The problem of how to capture and utilize rich interaction information of multi-type relations causes a major challenge of multilayer network embedding. To address this problem, we propose a fast and scalable multilayer network embedding model, called HMNE, to efficiently preserve and learn information of multi-type relations into a unified embedding space. We develop a heuristic 3D interactive walk technique dedicated for multilayer networks, which can leverage rich interactions among distinct layers and effectively capture important information contained in the layered structure. We evaluate our proposed model HMNE on two downstream analytic applications: node classification and link prediction. Experimental results on seven social and biological multilayer network datasets demonstrate that the proposed model outperforms existing competitive baselines with reduced time and memory occupations. [ABSTRACT FROM AUTHOR]

Many spatiotemporal events can be viewed as contagions. These events implicitly propagate across space and time by following cascading patterns, expanding their influence, and generating event cascades that involve multiple locations. Analyzing such cascading processes presents valuable implications in various urban applications, such as traffic planning and pollution diagnostics. Motivated by the limited capability of the existing approaches in mining and interpreting cascading patterns, we propose a visual analytics system called VisCas. VisCas combines an inference model with interactive visualizations and empowers analysts to infer and interpret the latent cascading patterns in the spatiotemporal context. To develop VisCas, we address three major challenges 1) generalized pattern inference; 2) implicit influence visualization; and 3) multifaceted cascade analysis. For the first challenge, we adapt the state-of-the-art cascading network inference technique to general urban scenarios, where cascading patterns can be reliably inferred from large-scale spatiotemporal data. For the second and third challenges, we assemble a set of effective visualizations to support location navigation, influence inspection, and cascading exploration, and facilitate the in-depth cascade analysis. We design a novel influence view based on a three-fold optimization strategy for analyzing the implicit influences of the inferred patterns. We demonstrate the capability and effectiveness of VisCas with two case studies conducted on real-world traffic congestion and air pollution datasets with domain experts. [ABSTRACT FROM AUTHOR]

Antagonistic crowd behaviors are often observed in cases of serious conflict. Antagonistic emotions, which is the typical psychological state of agents in different roles (i.e., cops, activists, and civilians) in crowd violence scenes, and the way they spread through contagion in a crowd are important causes of crowd antagonistic behaviors. Moreover, games, which refers to the interaction between opposing groups adopting different strategies to obtain higher benefits and less casualties, determine the level of crowd violence. We present an antagonistic crowd simulation model (ACSEE), which is integrated with antagonistic emotional contagion and evolutionary game theories. Our approach models the antagonistic emotions between agents in different roles using two components: mental emotion and external emotion. We combine enhanced susceptible-infectious-susceptible (SIS) and game approaches to evaluate the role of antagonistic emotional contagion in crowd violence. Our evolutionary game theoretic approach incorporates antagonistic emotional contagion through deterrent force, which is modelled by a mixture of emotional forces and physical forces defeating the opponents. Antagonistic emotional contagion and evolutionary game theories influence each other to determine antagonistic crowd behaviors. We evaluate our approach on real-world scenarios consisting of different kinds of agents. We also compare the simulated crowd behaviors with real-world crowd videos and use our approach to predict the trends of crowd movements in violence incidents. We investigate the impact of various factors (number of agents, emotion, strategy, etc.) on the outcome of crowd violence. We present results from user studies suggesting that our model can simulate antagonistic crowd behaviors similar to those seen in real-world scenarios. [ABSTRACT FROM AUTHOR]

Multi-task learning in Convolutional Neural Networks (CNNs) has led to remarkable success in a variety of applications of computer vision. Towards effective multi-task CNN architectures, recent studies automatically learn the optimal combinations of task-specific features at single network layers. However, they generally learn an unchanged operation of feature combination after training, regardless of the characteristic changes of task-specific features across different inputs. In this paper, we propose a novel Adaptive Feature Aggregation (AFA) layer for multi-task CNNs, in which a dynamic aggregation mechanism is designed to allow each task adaptively determines the degree to which the knowledge sharing or preserving between tasks is needed based on the characteristics of inputs. We introduce two types of aggregation modules to the AFA layer, which realize the adaptive feature aggregation by capturing the feature dependencies of different tasks along the channel and spatial axes, respectively. The AFA layer is a plug-and-play component with low parameter and computation overheads, and can be trained end-to-end along with backbone networks. For both pixel-level and image-level tasks, we empirically show that our approach substantially outperforms the previous state-of-the-art methods of multi-task CNNs. The code and models are available at https://github.com/zhenshen-mla/AFANet. [ABSTRACT FROM AUTHOR]

State-of-the-art object detectors usually progressively downsample the input image until it is represented by small feature maps, which loses the spatial information and compromises the representation of small objects. In this article, we propose a context-aware block net (CAB Net) to improve small object detection by building high-resolution and strong semantic feature maps. To internally enhance the representation capacity of feature maps with high spatial resolution, we delicately design the context-aware block (CAB). CAB exploits pyramidal dilated convolutions to incorporate multilevel contextual information without losing the original resolution of feature maps. Then, we assemble CAB to the end of the truncated backbone network (e.g., VGG16) with a relatively small downsampling factor (e.g., 8) and cast off all following layers. CAB Net can capture both basic visual patterns as well as semantical information of small objects, thus improving the performance of small object detection. Experiments conducted on the benchmark Tsinghua-Tencent 100K and the Airport dataset show that CAB Net outperforms other top-performing detectors by a large margin while keeping real-time speed, which demonstrates the effectiveness of CAB Net for small object detection. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has been envisioned as a promising paradigm that could effectively enhance the computational capacity of wireless user devices (WUDs) and quality of experience of mobile applications. One of the most crucial issues of MEC is computation offloading, which decides how to offload WUDs’ tasks to edge severs for further intensive computation. Conventional mathematical programming-based offloading approaches could face troubles in dynamic MEC environments due to the time-varying channel conditions (caused primarily by WUD mobility). To address the problem, reinforcement learning (RL) based offloading approaches have been proposed, which develop offloading policies by mapping MEC states to offloading actions. However, these approaches could fail to converge in large-scale MEC due to the exponentially-growing state and action spaces. In this article, we propose a novel online computation offloading approach that could effectively reduce task latency and energy consumption in dynamic MEC with large-scale WUDs. First, a RL-based computation offloading and energy transmission algorithm is proposed to accelerate the learning process. Then, a joint optimization method is adopted to develop the allocating algorithm, which obtains near-optimal solutions for energy and computation resources allocation. Simulation results show that the proposed approach can converge efficiently and achieve significant performance improvements over baseline approaches. [ABSTRACT FROM AUTHOR]

Crowd emotional contagion is affected by physical cyberspace and virtual cyberspace at the same time. In addition, the individual personality also plays an important role in crowd emotional contagion. However, few existing works have investigated the two factors jointly. To solve this problem, we propose a personalized virtual and physical cyberspace-based emotional contagion model (PVP-ECM) to simulate the process of crowd emotional contagion. First, we construct an individual emotion model and propose the personalized emotional contagion rules to consider the influences of the individual personality on crowd emotional contagion coupling the virtual and physical cyberspaces. Second, we use mean field theory to derive an evolution process and obtain mean field equations for the PVP-ECM. Then, we solve the PVP-ECM numerically using the finite difference method. Third, we construct a personalized BA scale-free network for the PVP-ECM simulation to further verify the stability of the model. Finally, we perform the PVP-ECM simulation and implement a simulation system to visualize the results of our theoretical analysis. The experimental results show that our approach can simulate the process of crowd emotional contagion more realistically. The proposed method can provide guidance for coping with the personalized crowd emotional contagion of public emergencies. [ABSTRACT FROM AUTHOR]

In this article, we attempt to achieve one-shot object detection by mimicking the human ability to learn new concepts under limited reference, which aims at detecting all object instances of an unseen class in a target image when given a query image of the same unseen class. However, this one-shot learning ability of human benefits from the fact that human brain can quickly extract and process the associated information between the query–target images, which is an issue for the one-shot object detection framework to overcome. Moreover, the feature extraction of the query class in target images is intractable due to the complex and diversified background of remote sensing images. To solve these issues, we propose a solo-to-collaborative dual-attention network (SCoDANet) to hierarchically (image itself/pairs) enhance image feature representations. It consists of three components: 1) solo-attention head that strengthens the compactness of intraclass feature representations of an image and avoids background interference by selectively aggregating the similar features from the spatial and channel dimensions, respectively; 2) dual coattention module that guides RPN to generate an expected set of region proposals related to the query class by mining the coinformation of each query–target feature pair; and 3) nonlinear matching that provides a measure of similarity between the query feature and proposals of the target image to further learn a more robust detector. Our extensive experiments over two benchmarks demonstrate the effectiveness of our method under the one-shot scenario of detecting seen and unseen object categories. [ABSTRACT FROM AUTHOR]

The spatial time series generated by city sensors allow us to observe urban phenomena like environmental pollution and traffic congestion at an unprecedented scale. However, recovering causal relations from these observations to explain the sources of urban phenomena remains a challenging task because these causal relations tend to be time-varying and demand proper time series partitioning for effective analyses. The prior approaches extract one causal graph given long-time observations, which cannot be directly applied to capturing, interpreting, and validating dynamic urban causality. This paper presents Compass, a novel visual analytics approach for in-depth analyses of the dynamic causality in urban time series. To develop Compass, we identify and address three challenges: detecting urban causality, interpreting dynamic causal relations, and unveiling suspicious causal relations. First, multiple causal graphs over time among urban time series are obtained with a causal detection framework extended from the Granger causality test. Then, a dynamic causal graph visualization is designed to reveal the time-varying causal relations across these causal graphs and facilitate the exploration of the graphs along the time. Finally, a tailored multi-dimensional visualization is developed to support the identification of spurious causal relations, thereby improving the reliability of causal analyses. The effectiveness of Compass is evaluated with two case studies conducted on the real-world urban datasets, including the air pollution and traffic speed datasets, and positive feedback was received from domain experts. [ABSTRACT FROM AUTHOR]

Time-series data-usually presented in the form of lines-plays an important role in many domains such as finance, meteorology, health, and urban informatics. Yet, little has been done to support interactive exploration of large-scale time-series data, which requires a clutter-free visual representation with low-latency interactions. In this paper, we contribute a novel line-segment-based KD-tree method to enable interactive analysis of many time series. Our method enables not only fast queries over time series in selected regions of interest but also a line splatting method for efficient computation of the density field and selection of representative lines. Further, we develop KD-Box, an interactive system that provides rich interactions, e.g., timebox, attribute filtering, and coordinated multiple views. We demonstrate the effectiveness of KD-Box in supporting efficient line query and density field computation through a quantitative comparison and show its usefulness for interactive visual analysis on several real-world datasets. [ABSTRACT FROM AUTHOR]

Increasing attention is being given to the modeling and simulation of traffic flow and crowd movement, two phenomena that both deal with interactions between pedestrians and cars in many situations. In particular, crowd simulation is important for understanding mobility and transportation patterns. In this paper, we propose an emotion-based crowd simulation model integrating physical strength consumption. Inspired by the theory of “the devoted actor,” the movements of each individual in our model are determined by modeling the influence of physical strength consumption and the emotion of panic. In particular, human physical strength consumption is computed using a physics-based numerical method. Inspired by the James-Lange theory, panic levels are estimated by means of an enhanced emotional contagion model that leverages the inherent relationship between physical strength consumption and panic. To the best of our knowledge, our model is the first method integrating physical strength consumption into an emotion-based crowd simulation model by exploiting the relationship between physical strength consumption and emotion. We highlight the performance on different scenarios and compare the resulting behaviors with real-world video sequences. Our approach can reliably predict changes in physical strength consumption and panic levels of individuals in an emergency situation. [ABSTRACT FROM AUTHOR]

Canals are constructed worldwidely to divert water from rich to arid areas to mitigate water shortages. Since water resource is fairly limited, it is essential to perform canal control efficiently to improve water-delivery performance. A promising solution is to leverage model predictive control (MPC), which calculates the desired canal action at each time step via reliable predictions of the model. However, the predictive model dependence degrades the practicability and the iterative calculation incurs intensive computations, especially for large-scale canals with high-dimensional state and action spaces (curse of dimensionality). This article presents a new canal control model named efficient model-free canal control (EMCC) that obtains control policies in a model-free way via deep reinforcement learning (DRL) and alleviates the curse of dimensionality via domain knowledge (control experience). EMCC adopts the hidden Markov model with Gaussian mixture densities (GMM-HMM) to model canal system dynamics with control experience, and initializes it according to the actual operation data. Besides, we design a reward generator collaborated with GMM-HMM to supervise the reinforcement learning around control experiences to obtain more efficient control policies. We evaluate EMCC via numerical simulations on Chinese largest water-delivery project (SNWTP). Experimental results show that EMCC leads to significant convergence performances compared with crude applications of DRL on large-scale canals, and achieves desired objectives more satisfactorily than MPC and control-experience under two typical water-delivery tasks. [ABSTRACT FROM AUTHOR]

To establish robust semantic correspondence between images covering different objects belonging to the same category, there are three important types of information including inter-image relationship, intra-image relationship and cycle consistency. Most existing methods only exploit one or two types of the above information and cannot make them enhance and complement each other. Different from existing methods, we propose a novel end-to-end Consistency Graph Modeling Network (CGMNet) for semantic correspondence by modeling inter-image relationship, intra-image relationship and cycle consistency jointly in a unified deep model. The proposed CGMNet enjoys several merits. First, to the best of our knowledge, this is the first work to jointly model the three kinds of information in a deep model for semantic correspondence. Second, our model has designed three effective modules including cross-graph module, intra-graph module and cycle consistency module, which can jointly learn more discriminative feature representations robust to local ambiguities and background clutter for semantic correspondence. Extensive experimental results show that our algorithm performs favorably against state-of-the-art methods on four challenging datasets including PF-PASCAL, PF-WILLOW, Caltech-101 and TSS. [ABSTRACT FROM AUTHOR]

Single Image Deraining (SID) is a relatively new and still challenging topic in emerging vision applications, and most of the recently emerged deraining methods use the supervised manner depending on the ground-truth (i.e., using paired data). However, in practice it is rather common to encounter unpaired images in real deraining task. In such cases, how to remove the rain streaks in an unsupervised way will be a challenging task due to lack of constraints between images and hence suffering from low-quality restoration results. In this paper, we therefore explore the unsupervised SID issue using unpaired data, and propose a new unsupervised framework termed DerainCycleGAN for single image rain removal and generation, which can fully utilize the constrained transfer learning ability and circulatory structures of CycleGAN. In addition, we design an unsupervised rain attentive detector (UARD) for enhancing the rain information detection by paying attention to both rainy and rain-free images. Besides, we also contribute a new synthetic way of generating the rain streak information, which is different from the previous ones. Specifically, since the generated rain streaks have diverse shapes and directions, existing derianing methods trained on the generated rainy image by this way can perform much better for processing real rainy images. Extensive experimental results on synthetic and real datasets show that our DerainCycleGAN is superior to current unsupervised and semi-supervised methods, and is also highly competitive to the fully-supervised ones. [ABSTRACT FROM AUTHOR]

In the few-shot common-localization task, given few support images without bounding box annotations at each episode, the goal is to localize the common object in the query image of unseen categories. The few-shot common-localization task involves common object reasoning from the given images, predicting the spatial locations of the object with different shapes, sizes, and orientations. In this work, we propose a common-centric localization (CCL) network for few-shot common-localization. The motivation of our common-centric localization network is to learn the common object features by dynamic feature relation reasoning via a graph convolutional network with conditional feature aggregation. First, we propose a local common object region generation pipeline to reduce background noises due to feature misalignment. Each support image predicts more accurate object spatial locations by replacing the query with the images in the support set. Second, we introduce a graph convolutional network with dynamic feature transformation to enforce the common object reasoning. To enhance the discriminability during feature matching and enable a better generalization in unseen scenarios, we leverage a conditional feature encoding function to alter visual features according to the input query adaptively. Third, we introduce a common-centric relation structure to model the correlation between the common features and the query image feature. The generated common features guide the query image feature towards a more common object-related representation. We evaluate our common-centric localization network on four datasets, i.e., CL-VOC-07, CL-VOC-12, CL-COCO, CL-VID. We obtain significant improvements compared to state-of-the-art. Our quantitative results confirm the effectiveness of our network. [ABSTRACT FROM AUTHOR]

This paper focuses on the thorax disease classification problem in chest X-ray (CXR) images. Different from the generic image classification task, a robust and stable CXR image analysis system should consider the unique characteristics of CXR images. Particularly, it should be able to: 1) automatically focus on the disease-critical regions, which usually are of small sizes; 2) adaptively capture the intrinsic relationships among different disease features and utilize them to boost the multi-label disease recognition rates jointly. In this paper, we propose to learn discriminative features with a two-branch architecture, named ConsultNet, to achieve those two purposes simultaneously. ConsultNet consists of two components. First, an information bottleneck constrained feature selector extracts critical disease-specific features according to the feature importance. Second, a spatial-and-channel encoding based feature integrator enhances the latent semantic dependencies in the feature space. ConsultNet fuses these discriminative features to improve the performance of thorax disease classification in CXRs. Experiments conducted on the ChestX-ray14 and CheXpert dataset demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article proposes a fast and accurate network for surface defect detection, termed SDDNet. SDDNet mainly addresses two challenging issues—large texture variation and small size of defects—by introducing two modules: feature retaining block (FRB) and skip densely connected module (SDCM). FRB fuses multiple pyramidal feature maps with different resolutions and is plugged on the top of pooling layers, aiming to preserve the texture information, which may be lost because of downsampling. SDCM is designed to propagate the fine-grained details from low- to high-level feature maps for better prediction of defects, especially small defects. Extensive experiments conducted on the publicly available data sets NEU-DET (88.8% mAP), DAGM (99.1% mAP), and Magnetic-Tile (93.4% mAP) have demonstrated the effectiveness of the proposed SDDNet and its feasibility for real-time industrial applications. [ABSTRACT FROM AUTHOR]

With the rapid development of sensing technologies, massive spatiotemporal data have been acquired from the urban space with respect to different domains, such as transportation and environment. Numerous co-occurrence patterns (e.g., traffic speed < 10km/h, weather = foggy, and air quality = unhealthy) between the transportation data and other types of data can be obtained with given spatiotemporal constraints (e.g., within 3 kilometers and lasting for 2 hours) from these heterogeneous data sources. Such patterns present valuable implications for many urban applications, such as traffic management, pollution diagnosis, and transportation planning. However, extracting and understanding these patterns is beyond manual capability because of the scale, diversity, and heterogeneity of the data. To address this issue, a novel visual analytics system called CorVizor is proposed to identify and interpret these co-occurrence patterns. CorVizor comprises two major components. The first component is a co-occurrence mining framework involving three steps, namely, spatiotemporal indexing, co-occurring instance generation, and pattern mining. The second component is a visualization technique called CorView that implements a level-of-detail mechanism by integrating tailored visualizations to depict the extracted spatiotemporal co-occurrence patterns. The case studies and expert interviews are conducted to demonstrate the effectiveness of CorVizor. [ABSTRACT FROM AUTHOR]