Graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks. However, existing CNNs generally use a fixed which may not be optimal In this paper, we propose novel Learning-Convolutional Network (GLCN) learning. The aim of GLCN is to learn an structure that best serves by integrating both convolution in unified network architecture. main advantage given labels the estimated are incorporated thus can provide useful...

How to perform effective information fusion of different modalities is a core factor in boosting the performance RGBT tracking. This paper presents novel deep algorithm based on representations from an end-to-end trained convolutional neural network. To deploy complementarity features all layers, we propose recursive strategy densely aggregate these that yield robust target objects each modality. In modalities, prune aggregated collaborative way. specific, employ operations global average...

RGBT tracking receives a surge of interest in the computer vision community, but this research field lacks large-scale and high-diversity benchmark dataset, which is essential for both training deep trackers comprehensive evaluation methods. To end, we present La rge- s cale H igh-diversity [Formula: see text]nchmark short-term R GBT (LasHeR) work. LasHeR consists 1224 visible thermal infrared video pairs with more than 730K frame total. Each pair spatially aligned manually annotated...

RGBT tracking has attracted increasing attention since RGB and thermal infrared data have strong complementary advantages, which could make trackers all-day all-weather work. However, how to effectively represent for visual remains unstudied well. Existing works usually focus on extracting modality-shared or modality-specific information, but the potentials of these two cues are not well explored exploited in tracking. In this paper, we propose a novel multi-adapter network jointly perform...

The competent software architecture plays a crucial role in the difficult task of big data processing for SQL and NoSQL databases. databases were created to organize allow horizontal expansion. databases, on other hand, support scalability can efficiently process large amounts unstructured data. Organizational needs determine which paradigm is appropriate, yet selecting best option not always easy. Differences database design are what set apart. Each type also consistently employs...

Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because the binary anomaly label only given on video level, but output requires snippet-level predictions. So, Multiple Instance Learning (MIL) prevailing in WSVAD. However, MIL notoriously known to suffer from many false alarms detector easily biased towards abnormal snippets with simple context, confused by normality same bias, and missing a different pattern. To this end, we propose new framework: Unbiased (UMIL), learn...

This paper describes an efficient algorithm for inexact graph matching. The method is purely structural, that is, it uses only the edge or connectivity structure of and does not draw on node attributes. We make two contributions: 1) commencing from a probability distribution matching errors, we show how problem can be posed as maximum-likelihood estimation using apparatus EM algorithm; 2) cast recovery correspondence matches between nodes in matrix framework. allows one to efficiently...

Graph structures have proven computationally cumbersome for pattern analysis. The reason this is that, before graphs can be converted to vectors, correspondences must established between the nodes of which are potentially different size. To overcome problem, in paper, we turn spectral decomposition Laplacian matrix. We show how elements matrix used construct symmetric polynomials that permutation invariants. coefficients these as graph features encoded a vectorial manner. extend...

Principal Component Analysis (PCA) is a widely used to learn low-dimensional representation. In many applications, both vector data X and graph W are available. Laplacian embedding for data. We propose graph-Laplacian PCA (gLPCA) low dimensional representation of that incorporates structures encoded in W. This model has several advantages: (1) It model. (2) compact closed-form solution can be efficiently computed. (3) capable remove corruptions. Extensive experiments on 8 datasets show...

Existing visual trackers are easily disturbed by occlusion, blur and large deformation. We think the performance of existing may be limited due to following issues: i) Adopting dense sampling strategy generate positive examples will make them less diverse; ii) The training data with different challenging factors limited, even through collecting dataset. Collecting larger dataset is most intuitive paradigm, but it still can not cover all situations samples monotonous. In this paper, we...

RGBT tracking becomes a popular computer vision task, and has variety of applications in visual surveillance systems, self-driving cars intelligent transportation system. This paper investigates how to perform robust adverse challenging conditions using complementary thermal infrared data (RGBT tracking). We propose novel deep network architecture called quality-aware Feature Aggregation Network (FANet) for tracking. Unlike existing trackers, our FANet aggregates hierarchical features within...

Fully Convolutional Neural Network (FCN) has been widely applied to salient object detection recently by virtue of high-level semantic feature extraction, but existing FCN-based methods still suffer from continuous striding and pooling operations leading loss spatial structure blurred edges. To maintain the clear edge objects, we propose a novel Edge-guided Non-local FCN (ENFNet) perform edge-guided learning for accurate detection. In specific, extract hierarchical global local information...

Despite much research progress in image semantic segmentation, it remains challenging under adverse environmental conditions caused by imaging limitations of the visible spectrum, while thermal infrared cameras have several advantages over for such as operating total darkness, insensitive to illumination variations, robust shadow effects, and strong ability penetrate haze smog. These make segmentation objects day night. In this article, we propose a novel network architecture, called...

A powerful machine learning detector based on the k-nearest neighbors (KNN) algorithm is proposed to overcome system impairments. The zero-dispersion link (ZDL), dispersion managed (DML), and unmanaged (DUL) are considered. Meanwhile, an improved algorithm, distance-weight KNN, introduced, which outperforms conventional maximum likelihood-post compensation approach. numerical results show that KNN feasible for overcoming various impairments, especially non-Gaussian symmetric noise, such as...

Remote sensing scene classification (RSSC) is a hotspot and play very important role in the field of remote image interpretation recent years. With development convolutional neural networks, significant breakthrough has been made scenes. Many objects form complex diverse scenes through spatial combination association, which makes it difficult to classify The problem insufficient differentiation feature representations extracted by Convolutional Neural Networks (CNNs) still exists, mainly due...

Perfluorooctanoic acid (PFOA) is a ubiquitous environmental pollutant suspected of being an endocrine disruptor; however, mechanisms male reproductive disorders induced by PFOA are poorly understood. In this study, mice were exposed to 0, 0.31, 1.25, 5, and 20 mg PFOA/kg/day oral gavage for 28 days. significantly damaged the seminiferous tubules reduced testosterone progesterone levels in testis dose-dependent manner. Furthermore, exposure sperm quality. We identified 93 differentially...

Image salient object detection (SOD) is an active research topic in computer vision and multimedia area. Fusing complementary information of RGB depth has been demonstrated to be effective for image which known as RGB-D problem. The main challenge how exploit the cues both intra-modality (RGB, depth) cross-modality simultaneously In this paper, we tackle by designing a novel Saliency Generative Adversarial Network (cmSalGAN). cmSalGAN aims learn optimal view-invariant consistent pixel-level...

In recent years, RGBT tracking has become a hot topic in the field of visual tracking, and made great progress. this paper, we propose novel Trident Fusion Network (TFNet) to achieve effective fusion different modalities for robust tracking. specific, deploy complementarity features all convolutional layers, recursive strategy densely aggregate these that yield representations target objects two modalities. Moreover, design trident architecture integrate fused both modality-specific...

3D convolutional neural networks (CNNs) have been demonstrated to be a powerful tool in hyperspectral images (HSIs) classification. However, using the conventional CNNs extract spectral–spatial feature for HSIs results too many parameters as plenty of spatial redundancy. To address this issue, paper, we first design multiscale convolution contextual different scales and then propose employ octave CNN which factorizes mixed maps by their frequency replace normal order reduce redundancy...

Salient object detection (SOD) in optical remote sense images (ORSIs) is a valuable and challenging task. The factors ORSI, such as background clutter, lighting shadows, imaging blur, low resolution, significantly degrade the completeness accuracy of salient objects. To handle this problem, we propose novel model to learn robust multiscale region features objects by simultaneously optimizing their boundaries. First, extract through hierarchical attention module. Second, generate boundary...