Neurocomputing

Recently, few-shot learning has attracted more and more attention. Generally, the fine-tuning-based few-shot learning framework contains two stages: i) In the pre-training stage, using base data to train the feature extractor; ii) In ...

This paper studies the neural network (NN) decentralized observer-based fault-tolerant control (FTC) design problem on the nonlinear interconnected fractional-order systems. The considered fractional-order systems have the non strict-...

Generalized zero-shot learning (GZSL) is a challenging problem which aims to recognize images from both seen and unseen classes. Existing research suffers from the bias problem, which means that the model tends to misclassify an unseen ...

One of the fundamental tasks when providing personalized tutoring services to learners in online learning systems, such as intelligent tutoring systems and massive open online courses, is the learner knowledge diagnosis (LKD). LKD ...

Visual dialog is a task that two agents complete a multi-round conversation based on an image, a caption, and dialog histories. Despite the recent progress, existing methods still undergo degradation on the condition of complex ...

Machine reading comprehension is an important topic in natural language processing. However, the redundancy of text and the diversity of answer types are neglected in existing machine reading comprehension. To tackle these issues, a ...

This paper considers the point-to-point consensus tracking control for a class of nonlinear multi-agent systems with completely unknown dynamics, where the consensus is concerned with some given desired points instead of the entire ...

Video procedural captioning aims to generate detailed descriptive captions for all steps in a long instructional video. The peculiarity of this problem is the procedural dependency between the events to generate consistent captions ...

To accurately estimate 3D human pose from monocular camera images, a large amount of 3D annotated data is required. However, obtaining 3D annotated data outside the laboratory is not easy. In the absence of such data, weakly-supervised ...

Collaborative filtering approach greatly promotes the development and application of personalized recommendation. In location-based social networks (LBSNs), the sparsity of check-in data is one of the main obstacles for traditional ...

In this paper, the gain-scheduled state estimation issue is investigated for a kind of complex networks subject to randomly occurring nonlinearities under bit-rate constraints. An array of random variables is introduced to govern the ...

This paper considers the leader-following bipartite consensus for a class of nonlinear multi-agent systems (MASs) subject to exogenous disturbances under directed fixed and switching topologies, respectively. Firstly, two new output ...

This paper is concerned with the partial-neuron-based finite-time state estimation problem for a class of artificial neural networks with time-varying delays. Measurements information from only a small fractional of the artificial ...

The flexible artistic creation of painting using deep neural networks has attracted lots of attention recently. Existing image-to-image translation approaches show powerful capabilities in producing photos between various domains. ...

• Aiming at the cross-domain fault diagnosis of rotating components, a sample-weighted joint adversarial network is proposed, which explores category ...

In recent years, adversarial-based deep domain adaptation (DDA) has attracted increasing attention in transferable fault diagnosis. However, most of the existing methods mainly focus on the elimination of global distribution ...

Automatic sleep staging technology is a current research hotspot in brain-computer interfaces, freeing sleep specialists from the time-consuming task of manually diagnosing sleep. It performs complex sleep staging tasks by ...

One of the major challenges facing Electroencephalogram (EEG) in biometric systems is the high time-variability of the brainwaves, especially across different sessions. Electrical muscle activity, or Electromyogram (EMG), is considered ...

Human action analysis is a critical yet challenging task for understanding diverse video content. Recently, to enable explainable reasoning of video actions, a spatio-temporal video graph structure was proposed to represent the video ...

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• A comprehensive survey on image watermarking schemes in different domains.
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With the rapid development and popularity of the Internet, multimedia security has become a general essential concern. Especially, as manipulation of digital images gets much easier, the challenges it brings to authentication ...

To achieve high value of classification results by using a limited number of training samples, designing a multi-view semi-supervised classification model is extremely urgent. Moreover, considering that graph learning can fully capture ...

Nano-scale devices are thought to intervene in natural life for a variety of responsibilities. For understanding the intrinsic communication of such nano-scale devices, software and hardware modalities have been introduced. Some of ...

• Neuroevolution to develop Deep Convolutional Neural Network (DCNN) for image classification.

Developing Deep Convolutional Neural Networks (DCNNs) for image classification is a complicated task that needs considerable effort and knowledge. By employing an evolutionary computation approach, one can automatically generate the ...

• Contrastive learning improves the data utilization of belief representations in POMDPs.

The key to solving the complex, partially observable Markov decision process (POMDP) with high-dimensional observations lies in explicit belief representations. However, the existing methods generally adopted the black-box model for ...

Visual tracking has always been an important research topic in the field of computer vision. During tracking, cluttered backgrounds, deformations, and occasional occlusion inevitably cause unpredictable appearance changes in the target,...

High-speed and high-reliability automated quality inspection is widely demanded in the electronics industry. Recently, deep learning theory combined with many non-destructive technologies shows superior performance for inspecting ...

We propose a penalization algorithm for functional linear regression models, where the coefficient function β is shrunk towards a data-driven shape template γ. To the best of our knowledge, we employ the nonzero centered L 2 penalty in ...

The guided search theory suggests that global sources of scenes are important for robust visual processing. In this study, we use an eye-tracking experiment to verify that contours play a crucial role in guiding visual attention. ...

• A Slow feature network (SFNet) method is proposed for dynamics extraction and parallel linear and nonlinear monitoring.

In a typical industrial process, there may exist both linear and nonlinear relationships among process variables. Besides, the existence of process dynamics poses challenges to process monitoring. Some monitoring methods have been ...

Deep neural networks (DNNs) have achieved great success in the field of computer vision in recent years. While being high-precision, the characteristic of over-parameterization impedes DNNs from being applied to lightweight devices. To ...