Skeleton-based action recognition has been extensively studied, but it remains an unsolved problem because of the complex variations skeleton joints in 3-D spatiotemporal space. To handle this issue, we propose a newly temporal-then-spatial recalibration method named memory attention networks (MANs) and deploy MANs using temporal module (TARM) convolution (STCM). In TARM, novel mechanism is built based on residual learning to recalibrate frames data temporally. STCM, recalibrated sequence...

High-performance NiO@C electrochromism with quick electron-transfer and its application in EC eyewear were realized by regulation of carbon residues MOF derivatives.

The advancement of deep convolutional neural networks (DCNNs) has driven significant improvement in the accuracy recognition systems for many computer vision tasks. However, their practical applications are often restricted resource-constrained environments. In this paper, we introduce projection (PCNNs) with a discrete back propagation via (DBPP) to improve performance binarized (BNNs). contributions our paper include: 1) first time, function is exploited efficiently solve problem, which...

Skeleton-based action recognition task is entangled with complex spatio-temporal variations of skeleton joints, and remains challenging for Recurrent Neural Networks (RNNs). In this work, we propose a temporal-then-spatial recalibration scheme to alleviate such variations, resulting in an end-to-end Memory Attention (MANs) which consist Temporal Recalibration Module (TARM) Spatio-Temporal Convolution (STCM). Specifically, the TARM deployed residual learning module that employs novel...

Vehicle detection with category inference on video sequence data is an important but challenging task for urban traffic surveillance. The difficulty of this lies in the fact that it requires accurate localization relatively small vehicles complex scenes and expects real-time detection. In paper, we present a vehicle framework improves performance conventional Single Shot MultiBox Detector (SSD), which effectively detects different types real-time. Our approach, proposes use feature...

While intrinsic data structure in subspace provides useful information for visual recognition, it has not yet been well studied deep feature learning action recognition. In this paper, we introduce a new spatio-temporal manifold network (STMN) that leverages structures to regularize learning, aiming at simultaneously minimizing the intra-class variations of learned features and alleviating over-fitting problem. To end, prior is imposed from top layer convolutional neural (CNN), propagated...

Despite great effectiveness of very deep and wide Convolutional Neural Networks (CNNs) in various computer vision tasks, the significant cost terms storage requirement such networks impedes deployment on computationally limited devices. In this paper, we propose new modulated convolutional (MCNs) to improve portability CNNs via binarized filters. MCNs, a loss function which considers filter loss, center softmax an end-to-end framework. We first introduce modulation filters (M-Filters)...

Convolution sparse coding (CSC) has attracted much attention recently due to its advantages in image reconstruction and enhancement. However, the process suffers from perturbations caused by variations of input samples, as consistence features similar samples are not well addressed existing literature. In this paper, we will tackle feature problem a set via proposed manifold constrained convolutional (MCSC) method. The core idea MCSC is use intrinsic (Laplacian) structure data regularize...

A wide variety of vehicle detection approaches using the deep convolutional neural network (CNN) have achieved great success in recent years. However, existing CNN-based feature extraction algorithms, especially residual network, cannot obtain powerful semantic information task, and thus suffer from problem a missing detection, error or repeated detection. In this paper, we present connect-and-merge (CMNet) for fast detecting vehicles complex scenes. First, propose (CMRN) performing...

Ge2Sb2Te5 (GST) is a kind of non-volatile chalcogenide phase-change material, which has significant difference in permittivity between its amorphous and crystalline states the infrared range. On account this remarkable property, combination GST metamaterials great potential tunable meta-devices. In paper, perfect absorber based on nanocross-resonator array stacked above spacer layer an Au mirror (i.e., metal-dielectric-metal configuration) designed experimentally demonstrated. A thin indium...

Detecting cracks on the concrete surface is crucial for tunnel health monitoring and maintenance of Chinese transport facilities, since it closely related with structural reliability. The automated efficient crack detection recently has attracted more research studies, particularly cheap availability digital cameras makes this issue easier. However, still a challenging task due to blebs, stains, illumination over surface. This paper presents an method in structure based image processing deep...

Ground Penetrating Radar (GPR) is a shallow geophysical method for detecting and locating subsurface targets. The GPR image echo characteristics of complex underground structures can be obtained by carrying out forward modeling research. traditional finite-difference time-domain (FDTD) has low efficiency accuracy. alternating direction implicit FDTD (ADI-FDTD) algorithm surmounts the stability limitations method, making it possible to select larger time step higher computational efficiency....

In deep convolutional neural networks (DCNN), the pooling operation is usually adopted to produce condensed and transformation invariant feature maps for input image. However, it will inevitably induce information loss, which has not be addressed yet in designing filters of DCNNs. this paper, we propose Gaussian transfer (GT-CNN), introduce pool our GT-CNN, on features can transferred filters, are achieved same end-to-end framework. More importantly, multiple scales orientations further...

Abstract Semantic segmentation plays a significant role in histopathology by assisting pathologists diagnosis. Although fully-supervised learning achieves excellent success on for histopathological images, it costs and experts great efforts pixel-level annotation the meantime. Thus, to reduce workload, we proposed weakly-supervised framework called CAM-TMIL, which assembles methods based class activation maps (CAMs) multiple instance (MIL) perform with image-level labels. By leveraging MIL...

Pipeline fault detection is very important application of pipeline robots for the security underground drainage facilities. The performance existing systems closely related to image definition in complex environment terms darkness, water fog, haze, etc. In this paper, techniques dark channel prior and cloud processing are combined into framework haze removal system. system, including user management module, system sitting cloud-based module we transmit data with secure control mechanism,...

This issue is a typical NP-hard problem for an unrelated parallel machine scheduling with makespan minimization as the goal and no sequence-related preparation time.Based on idea of tabu search (TS), this paper improves iterative greedy algorithm (IG) proposes IG-TS deconstruction, reconstruction, neighborhood operations main optimization process.This has characteristics strong capability global fast speed convergence.The warp knitting workshop in textile industry, which complex large scale,...